What is the purpose of a feature article?

To inform, entertain, or persuade readers on a specific topic.

What is the difference between a news article and a feature article?

A news article reports on current events in a factual and objective manner, while a feature article delves deeper into a specific topic or person using more descriptive and engaging language.

A feature article focuses only on entertainment

A news article is longer than a feature article

Why is it important to use descriptive language in a feature article?

To paint a vivid picture and engage the reader's senses

What is the purpose of a headline in a feature article?

To grab the reader's attention and provide a glimpse of the article's main idea

To summarize the entire article

Feature It | Quizalize

Here is the survey with all bold text removed: --- Survey: Feedback on Noones' New Liquidity-Providing Outsourcing Tool We’re excited to introduce a new feature at Noones.com, allowing users to create buy and sell offers without needing upfront capital. By partnering with liquidity providers, users can earn passive income by setting markups on trades that are automatically fulfilled by our providers. Your feedback will help us refine this feature and understand its potential to benefit our users. Thank you for your time and insights! 1. Are you interested in a feature that allows you to create buy/sell offers without holding crypto or capital, by outsourcing fulfillment to liquidity providers? - [ ] Very interested - [ ] Somewhat interested - [ ] Neutral - [ ] Not very interested - [ ] Not interested at all 2. How likely are you to use a feature that lets you set markup rates on trades that are then automatically fulfilled by liquidity providers? - [ ] Very likely - [ ] Likely - [ ] Neutral - [ ] Unlikely - [ ] Very unlikely 3. If available, how often would you consider creating offers using the liquidity provider option? - [ ] Daily - [ ] Weekly - [ ] Monthly - [ ] Occasionally - [ ] Not interested in creating offers 4. How valuable do you find the following aspects of the liquidity-providing feature? - Earning passive income without capital investment - [ ] Very valuable - [ ] Valuable - [ ] Neutral - [ ] Not valuable - Setting custom markups and earning the difference - [ ] Very valuable - [ ] Valuable - [ ] Neutral - [ ] Not valuable - Automated trading with hands-free fulfillment - [ ] Very valuable - [ ] Valuable - [ ] Neutral - [ ] Not valuable 5. Would a feature like this make you more likely to recommend Noones to friends or colleagues? - [ ] Definitely - [ ] Probably - [ ] Not sure - [ ] Probably not - [ ] Definitely not 6. What would be your primary motivation for using this feature? - [ ] Earning passive income - [ ] Low barrier to entry (no capital required) - [ ] Scalability and flexibility in setting markups - [ ] Reliable, hands-free trading - [ ] All of the above 7. Do you have any concerns about this feature? (Select all that apply) - [ ] Security of trades and transactions - [ ] Understanding the markup and fee structure - [ ] Reliability of liquidity provider fulfillment - [ ] Potential profits or earnings - [ ] Other: _____________ 8. How likely are you to use Noones as your primary trading platform if this feature is implemented? - [ ] Very likely - [ ] Likely - [ ] Neutral - [ ] Unlikely - [ ] Very unlikely 9. How confident are you that this feature could increase your trading profits? - [ ] Very confident - [ ] Confident - [ ] Neutral - [ ] Not very confident - [ ] Not confident at all 10. Please share any additional thoughts on how this feature could enhance your experience with Noones, or any improvements you’d like to see. - ______________________________________________________________ Thank you for helping us make Noones better! Your feedback is invaluable in shaping features that support your trading goals and enhance your experience with us.

Match the word to its synonym level B1 CEFR. Use the vocabulary exactly adverb precisely except that aside from exist verb to be real existing adjective real, current Example: Flying cars are not practical with existing technology. existence noun reality Example: The existence of black holes has been confirmed by indirect observation. extraordinary adjective unusual feature noun important part of something Example: The Ramon Crater is a unique feature of the Negev Desert. feedback noun reaction figure noun shape Example: I can’t tell if that figure in the shadows is a man or a woman. figure out verb understand Example: I just can’t figure out how the magician did that amazing trick. financial adjective related to money Example: Her family is having financial problems so they can’t travel overseas this year. finance verb pay for Example: If I can’t get a loan from the bank, I won’t be able to finance a new apartment. finance noun money Example: An expert in finance predicts a global recession. finding/findings noun discoveries; results of a study Example: According to the findings of the police investigation, this is the gun which fired the fatal bullet. flexibility noun willingness to change flexible adjective adjusts easily Example: I’d prefer to meet on Monday morning but I can be flexible depending upon your schedule. flood noun a lot of water flood verb to cover with too much water flu noun type of sickness focus on/upon verb pay attention to Example: You should focus on your schoolwork if you want to improve your grades. focus noun attention People with attention deficit disorder lose focus easily. frequency noun how often frequent adjective very often Example: Hanah is a frequent customer and everyone at the store knows her. fresh adjective new Example: We need some fresh ideas if we’re going to solve this problem. frighten verb scare from preposition position, starting point gain verb make an increase, profit, earn Example: I have nothing to gain by choosing sides so I shall remain neutral. gain noun profit, amount earned generate verb create, make Example: Chat GPT can generate text written in any style you choose. guidance noun help, advice hopeful adjective optimistic, having a positive outlook Example: The farmers are hopeful that we will have rain this winter. hopefully adjective with luck ideal adjective best, most preferable Example: Nuclear power may not be an ideal solution to global warming, but it’s certainly worth considering. illness noun sickness, disease illustrate verb draw pictures illustration noun picture, image Example: Children’s storybooks have colorful illustrations. image noun picture, especially on film or television Example: The mother of the pop singer cried when she first saw her daughter’s image on television. in preposition within, inside, into in terms of regarding Example: That company makes a great product but they’re lacking in terms of customer service. in actual fact in truth Example: The mayor says the city is a safe place to live, but in actual fact the violent crime rate is very high. in connection with about Example: Police arrested four men in connection with the robbery. in that case if that is true Example: Billy Bob: “Traffic could be heavy tomorrow.” Peggy Sue: “In that case, we better leave early.” in the meantime while, during Example: The new computers won’t arrive until next week, but we can keep using the old ones in the meantime. initial adjective first Example: Her initial reaction to that song was negative, but over time she’s come to like it. initially adverb at first instruction noun teaching, order Example: Most new electronic devices come with a set of instructions. intelligence noun smartness Example: Since you have a degree from a good university, I assume you have sufficient intelligence to understand this problem. intelligent adjective smart Example: Joe isn’t very intelligent, but he is a kind person with a warm heart. interest noun attraction Example: Yossi has little interest in politics, whereas his wife goes to all the protests and demonstrations. interest verb to attract Example: Sports don’t really interest me, but my brother is a big basketball fan. introduce verb to show something new Example: Today in class I will introduce the basic concepts of literary analysis. invest verb to put money into something in order to earn money Example: Joe invested in cryptocurrency and lost a lot of money. investor noun one who puts money into something in order to earn money Example: Venture capitalists are investors who put money into risky start-up businesses. investment noun putting money into something in order to earn money Example: Buying real estate in Israel is a very safe investment because the value never goes down. investigate verb research, study Example: The police collected evidence to investigate the murder. investigation noun study Example: The police don’t have a suspect for the murder as the investigation isn’t finished yet. investigator noun detective Example: Detective Schmendrick is the lead investigator for the murder case. just about almost Example: I’m just about done here so I’ll be there shortly. keep on doing verb continue Example: You’re crazy if you keep on doing the same thing and expect different results. kind of type of Example: What kind of dog is that, a poodle? knowledge noun awareness Example: John failed the test due to lack of knowledge of the material. lack verb not having, missing Example: John failed the test due to lack of knowledge of the material. landscape noun the view of the land likely adjective, adverb probably Example: When we learn from our mistakes, we’re not likely to forget. limited adjective restricted Example: We should go to the store today because the sale is for a limited time only. limitation noun restriction little adjective small, not a lot Example: She always tells the truth. I have little reason to doubt her. look at verb see Example: People used to read newspapers on the train. Nowadays they just look at their phones. low adverb to a small amount or level Example: I have to charge my phone because the battery is running low. material noun documents, information Example: We have a lot of material to cover before the end of the semester. meaning noun significance mean verb to have significance or purpose means noun form of, by the use of Example: They communicate by means of radio. measure noun step Example: The teacher took measures to prevent cheating during the test mention verb to say, point out Example: The coach said the team played very well today but didn’t mention any player specifically. miss verb (1) fail to catch (2) wishing to see somebody Examples: (1) The football player kicked the ball but missed the goal. (2) Wow, it’s good to see you! I’ve missed you so much! misunderstand verb understand incorrectly Example: I’m afraid I misunderstood the instructions. Could you repeat them please? more or less approximately, somewhat, to a varying degree Example: This is more or less a religious neighborhood, though there are a few secular families. must modal verb have to naturally adverb as expected, normally nature noun (1) open air (2) character Examples: (1) We like to go hiking in nature reserves. (2) Pit bulls are aggressive by nature.

The Northeast is characterized by its long coast along the Atlantic Ocean, making it a defining landform of the region. There are no deserts or Rocky Mountains in the Northeast, and the Mississippi River does not play a significant role in the region's geography. A sound is a body of water that separates a mainland and an island. For example, Long Island Sound is located between New York and Connecticut. The coastal areas of the Northeast are known for their many islands and attract more visitors and tourists compared to the mountain areas. The mountain areas, on the other hand, are made up of older landforms and ranges such as the Appalachian Mountains. Evidence of glaciers once covering the Northeast includes the numerous lakes and ponds found in the mountain areas, which were left behind after the glaciers receded. The Northeast region’s four largest rivers drain into the Atlantic Ocean. This geographic feature contributed to the development of major cities along the Atlantic coast, as these rivers provided access for transportation and trade. Maple syrup is an example of a forest resource found in the Northeast region. It is made from the sap of sugar maple trees, which thrive in the region’s forests. New Hampshire is nicknamed the Granite State because of its rock quarries, which have been a significant part of the state’s history and economy. Vermont is well-known for its dairy farms, while Massachusetts is famous for its cranberry bogs, which are a unique feature of the state’s agriculture. Snow is considered a resource in the Northeast because it attracts skiers and tourists to states like Vermont, boosting the local economy. Tourism in the Northeast is most directly influenced by attractions such as historic homes in Rhode Island, which showcase the region’s rich history and culture.

Use the questions and answers below to make a 10 question quiz: Which principle of interactive media is most impacted by its ‘ubiquity’? A) Restricted access control B) Global market presence and integration C) Limited reach to specific user demographics D) Single-channel broadcasting Answer: B Which feature of interactive media ensures that users can actively control and manipulate the content they access? A) Multimedia integration B) UI simplification C) User interactivity D) System automation Answer: C How does globalisation enhance user experience in interactive media systems, according to 1.1.1? A) By reducing content to a single cultural standard B) By supporting diverse user needs through varied, accessible content C) By enforcing a common global pricing structure D) By allowing one-way communication only Answer: B Why is copyright compliance critical in the context of interactive media? A) It allows users to freely distribute content B) It supports ethical use and protects creators from unauthorized distribution C) It restricts all users from accessing online media D) It focuses solely on preventing duplication of digital games Answer: B In interactive media, 'UX' primarily focuses on what aspect of user engagement? A) Monitoring users’ online activities B) Enabling dynamic user experiences tailored to user intent and satisfaction C) Simplifying multimedia formats to save storage D) Reducing user interaction to maintain control Answer: B What is a key distinction between ‘social issues’ and ‘ethical issues’ in interactive media development? A) Social issues focus on technical concerns, while ethical issues are user-centered B) Social issues relate to user interactions, while ethical issues focus on moral responsibilities C) Social issues address individual rights, whereas ethical issues involve systemic improvements D) Social issues are about compliance, whereas ethical issues concern legal standards Answer: B Which component is crucial to creating an accessible interactive media system for users with disabilities? A) High-end processing units B) Customizable UI elements, such as voice and visual aids C) Limited content based on user demographics D) Exclusive copyright protections Answer: B How does data quality contribute to an interactive media system's success? A) By maximizing data storage for multimedia content B) By ensuring content is relevant, current, and accurate for the intended audience C) By focusing on visual appeal over functionality D) By reducing interaction requirements to enhance performance Answer: B Which factor in interactive media systems enhances global engagement through a seamless user experience? A) Interactivity B) Ubiquity C) Restricted Access D) Content Redundancy Answer: B Why might legal implications arise in interactive media systems despite ethical intentions? A) Compliance with global standards B) Misinterpretations of user intent in a diverse cultural context C) Excessive content validation D) Limited user feedback channels Answer: B

DESCRIPTIVE TEXT Definition of Descriptive Text Descriptive Text is a text which says what a person or a thing is like. Its purpose is to describe and reveal a particular person, place, or thing.” Generic Structure of Descriptive Text # Identification : Identifies phenomenon (person, place, or thing) that will be described. (berisi tentang identifikasi hal / seorang yang akan dideskripsikan.) # Description : Describes parts, qualities, characteristics, etc (berisi tentang penjelasan / penggambaran tentang hal / seseorang dengan menyebutkan beberapa sifatnya.) The Characteristics / Language Feature of Descriptive Text 1. The use of adjective to clarify the noun, for example: a beautiful beach, a handsome man, the famous place in jepara, etc. 2. Focus on specific participant, has a ertain object , is not common, for example Sadengan Beach, Borobudur Temple,Christiano Ronaldo, etc 3. The use of simple present tense: The sentence pattern used is simple present because it tells the fact of the object described. 4. Action verb : verbs that show an activity( for example ; run, walk, sleep, etc)

“There’s No Such Thing as Sound Science” by By Christie Aschwanden was a lead science writer for FiveThirtyEight. FiveThirtyEight, Science, Dec. 6, 2017 Science is being turned against itself. For decades, its twin ideals of transparency and rigor have been weaponized by those who disagree with results produced by the scientific method. Under the Trump administration, that fight has ramped up again. In a move ostensibly meant to reduce conflicts of interest, Environmental Protection Agency Administrator Scott Pruitt has removed a number of scientists from advisory panels and replaced some of them with representatives from industries that the agency regulates. Like many in the Trump administration, Pruitt has also cast doubt on the reliability of climate science. For instance, in an interview with CNBC, Pruitt said that “measuring with precision human activity on the climate is something very challenging to do.” Similarly, Trump’s pick to head NASA, an agency that oversees a large portion the nation’s climate research, has insisted that research into human influence on climate lacks certainty, and he falsely claimed that “global temperatures stopped rising 10 years ago.” Kathleen Hartnett White, Trump’s nominee to head the White House Council on Environmental Quality, said in a Senate hearing last month that she thinks we “need to have more precise explanations of the human role and the natural role” in climate change. The same entreaties crop up again and again: We need to root out conflicts. We need more precise evidence. What makes these arguments so powerful is that they sound quite similar to the points raised by proponents of a very different call for change that’s coming from within science. This other movement strives to produce more robust, reproducible findings. Despite having dissimilar goals, the two forces espouse principles that look surprisingly alike: Science needs to be transparent. Results and methods should be openly shared so that outside researchers can independently reproduce and validate them. The methods used to collect and analyze data should be rigorous and clear, and conclusions must be supported by evidence. These are the arguments underlying an “open science” reform movement that was created, in part, as a response to a “reproducibility crisis” that has struck some fields of science.1 But they’re also used as talking points by politicians who are working to make it more difficult for the EPA and other federal agencies to use science in their regulatory decision-making, under the guise of basing policy on “sound science.” Science’s virtues are being wielded against it. What distinguishes the two calls for transparency is intent: Whereas the “open science” movement aims to make science more reliable, reproducible and robust, proponents of “sound science” have historically worked to amplify uncertainty, create doubt and undermine scientific discoveries that threaten their interests. “Our criticisms are founded in a confidence in science,” said Steven Goodman, co-director of the Meta-Research Innovation Center at Stanford and a proponent of open science. “That’s a fundamental difference — we’re critiquing science to make it better. Others are critiquing it to devalue the approach itself.” Calls to base public policy on “sound science” seem unassailable if you don’t know the term’s history. The phrase was adopted by the tobacco industry in the 1990s to counteract mounting evidence linking secondhand smoke to cancer. A 1992 Environmental Protection Agency report identified secondhand smoke as a human carcinogen, and Philip Morris responded by launching an initiative to promote what it called “sound science.” In an internal memo, Philip Morris vice president of corporate affairs Ellen Merlo wrote that the program was designed to “discredit the EPA report,” “prevent states and cities, as well as businesses from passing smoking bans” and “proactively” pass legislation to help their cause. The sound science tactic exploits a fundamental feature of the scientific process: Science does not produce absolute certainty. Contrary to how it’s sometimes represented to the public, science is not a magic wand that turns everything it touches to truth. Instead, it’s a process of uncertainty reduction, much like a game of 20 Questions. Any given study can rarely answer more than one question at a time, and each study usually raises a bunch of new questions in the process of answering old ones. “Science is a process rather than an answer,” said psychologist Alison Ledgerwood of the University of California, Davis. Every answer is provisional and subject to change in the face of new evidence. It’s not entirely correct to say that “this study proves this fact,” Ledgerwood said. “We should be talking instead about how science increases or decreases our confidence in something.” The tobacco industry’s brilliant tactic was to turn this baked-in uncertainty against the scientific enterprise itself. While insisting that they merely wanted to ensure that public policy was based on sound science, tobacco companies defined the term in a way that ensured that no science could ever be sound enough. The only sound science was certain science, which is an impossible standard to achieve. “Doubt is our product,” wrote one employee of the Brown & Williamson tobacco company in a 1969 internal memo. The note went on to say that doubt “is the best means of competing with the ‘body of fact’” and “establishing a controversy.” These strategies for undermining inconvenient science were so effective that they’ve served as a sort of playbook for industry interests ever since, said Stanford University science historian Robert Proctor. The sound science push is no longer just Philip Morris sowing doubt about the links between cigarettes and cancer. It’s also a 1998 action plan by the American Petroleum Institute, Chevron and Exxon Mobil to “install uncertainty” about the link between greenhouse gas emissions and climate change. It’s industry-funded groups’ late-1990s effort to question the science the EPA was using to set fine-particle-pollution air-quality standards that the industry didn’t want. And then there was the more recent effort by Dow Chemical to insist on more scientific certainty before banning a pesticide that the EPA’s scientists had deemed risky to children. Now comes a move by the Trump administration’s EPA to repeal a 2015 rule on wetlands protection by disregarding particular studies. (To name just a few examples.) Doubt merchants aren’t pushing for knowledge, they’re practicing what Proctor has dubbed “agnogenesis” — the intentional manufacture of ignorance. This ignorance isn’t simply the absence of knowing something; it’s a lack of comprehension deliberately created by agents who don’t want you to know, Proctor said.2 In the hands of doubt-makers, transparency becomes a rhetorical move. “It’s really difficult as a scientist or policy maker to make a stand against transparency and openness, because well, who would be against it?” said Karen Levy, researcher on information science at Cornell University. But at the same time, “you can couch everything in the language of transparency and it becomes a powerful weapon.” For instance, when the EPA was preparing to set new limits on particulate pollution in the 1990s, industry groups pushed back against the research and demanded access to primary data (including records that researchers had promised participants would remain confidential) and a reanalysis of the evidence. Their calls succeeded and a new analysis was performed. The reanalysis essentially confirmed the original conclusions, but the process of conducting it delayed the implementation of regulations and cost researchers time and money. Delay is a time-tested strategy. “Gridlock is the greatest friend a global warming skeptic has,” said Marc Morano, a prominent critic of global warming research and the executive director of ClimateDepot.com, in the documentary “Merchants of Doubt” (based on the book by the same name). Morano’s site is a project of the Committee for a Constructive Tomorrow, which has received funding from the oil and gas industry. “We’re the negative force. We’re just trying to stop stuff.” Some of these ploys are getting a fresh boost from Congress. The Data Quality Act (also known as the Information Quality Act) was reportedly written by an industry lobbyist and quietly passed as part of an appropriations bill in 2000. The rule mandates that federal agencies ensure the “quality, objectivity, utility, and integrity of information” that they disseminate, though it does little to define what these terms mean. The law also provides a mechanism for citizens and groups to challenge information that they deem inaccurate, including science that they disagree with. “It was passed in this very quiet way with no explicit debate about it — that should tell you a lot about the real goals,” Levy said. But what’s most telling about the Data Quality Act is how it’s been used, Levy said. A 2004 Washington Post analysis found that in the 20 months following its implementation, the act was repeatedly used by industry groups to push back against proposed regulations and bog down the decision-making process. Instead of deploying transparency as a fundamental principle that applies to all science, these interests have used transparency as a weapon to attack very particular findings that they would like to eradicate. Now Congress is considering another way to legislate how science is used. The Honest Act, a bill sponsored by Rep. Lamar Smith of Texas,3 is another example of what Levy calls a “Trojan horse” law that uses the language of transparency as a cover to achieve other political goals. Smith’s legislation would severely limit the kind of evidence the EPA could use for decision-making. Only studies whose raw data and computer codes were publicly available would be allowed for consideration. That might sound perfectly reasonable, and in many cases it is, Goodman said. But sometimes there are good reasons why researchers can’t conform to these rules, like when the data contains confidential or sensitive medical information.4 Critics, which include more than a dozen scientific organizations, argue that, in practice, the rules would prevent many studies from being considered in EPA reviews.5 It might seem like an easy task to sort good science from bad, but in reality it’s not so simple. “There’s a misplaced idea that we can definitively distinguish the good from the not-good science, but it’s all a matter of degree,” said Brian Nosek, executive director of the Center for Open Science. “There is no perfect study.” Requiring regulators to wait until they have (nonexistent) perfect evidence is essentially “a way of saying, ‘We don’t want to use evidence for our decision-making,’” Nosek said. Most scientific controversies aren’t about science at all, and once the sides are drawn, more data is unlikely to bring opponents into agreement. Michael Carolan, who researches the sociology of technology and scientific knowledge at Colorado State University, wrote in a 2008 paper about why objective knowledge is not enough to resolve environmental controversies. “While these controversies may appear on the surface to rest on disputed questions of fact, beneath often reside differing positions of value; values that can give shape to differing understandings of what ‘the facts’ are.” What’s needed in these cases isn’t more or better science, but mechanisms to bring those hidden values to the forefront of the discussion so that they can be debated transparently. “As long as we continue down this unabashedly naive road about what science is, and what it is capable of doing, we will continue to fail to reach any sort of meaningful consensus on these matters,” Carolan writes. The dispute over tobacco was never about the science of cigarettes’ link to cancer. It was about whether companies have the right to sell dangerous products and, if so, what obligations they have to the consumers who purchased them. Similarly, the debate over climate change isn’t about whether our planet is heating, but about how much responsibility each country and person bears for stopping it. While researching her book “Merchants of Doubt,” science historian Naomi Oreskes found that some of the same people who were defending the tobacco industry as scientific experts were also receiving industry money to deny the role of human activity in global warming. What these issues had in common, she realized, was that they all involved the need for government action. “None of this is about the science. All of this is a political debate about the role of government,” she said in the documentary. These controversies are really about values, not scientific facts, and acknowledging that would allow us to have more truthful and productive debates. What would that look like in practice? Instead of cherry-picking evidence to support a particular view (and insisting that the science points to a desired action), the various sides could lay out the values they are using to assess the evidence. For instance, in Europe, many decisions are guided by the precautionary principle — a system that values caution in the face of uncertainty and says that when the risks are unclear, it should be up to industries to show that their products and processes are not harmful, rather than requiring the government to prove that they are harmful before they can be regulated. By contrast, U.S. agencies tend to wait for strong evidence of harm before issuing regulations. Both approaches have critics, but the difference between them comes down to priorities: Is it better to exercise caution at the risk of burdening companies and perhaps the economy, or is it more important to avoid potential economic downsides even if it means that sometimes a harmful product or industrial process goes unregulated? In other words, under what circumstances do we agree to act on a risk? How certain do we need to be that the risk is real, and how many people would need to be at risk, and how costly is it to reduce that risk? Those are moral questions, not scientific ones, and openly discussing and identifying these kinds of judgment calls would lead to a more honest debate. Science matters, and we need to do it as rigorously as possible. But science can’t tell us how risky is too risky to allow products like cigarettes or potentially harmful pesticides to be sold — those are value judgements that only humans can make.

### Quizalize Script: **"Millennial TV & Celeb Quiz Challenge"** #### Quiz Introduction: - **Title**: "Millennial TV & Celebrity Quiz Challenge 📺✨" - **Description**: "Are you ready to prove your 2000s pop culture expertise? Answer these fun questions to show off your TV and celebrity knowledge! Invite your friends and see who comes out on top!" - **Gamified Element**: - Enable Quizalize’s leaderboard feature to track scores as users compete in real-time. #### Question 1: - **Prompt**: "Who said this iconic line? *‘How YOU doin’?’* 👀" - **Answer Options**: 1. Ross (Friends) 2. Joey (Friends) 3. Chandler (Friends) - **Correct Answer**: Joey (Friends) - **Feedback**: - **Correct**: "🎉 You got it! Joey Tribbiani’s catchphrase is unforgettable. 👏🍕" - **Incorrect**: "❌ Oops! It’s Joey from *Friends*! Now we need to binge-watch, don’t we?" - **Gamified Feature**: Award bonus points for quick answers, encouraging fast responses. #### Question 2: - **Prompt**: "Which show features these 4 iconic New Yorkers? 👠✨" - **Answer Options**: 1. Gossip Girl 2. The OC 3. Sex and the City - **Correct Answer**: Sex and the City - **Feedback**: - **Correct**: "👏 Yesss! It’s *Sex and the City*! Samantha, Carrie, Miranda, and Charlotte are forever icons. 💅" - **Incorrect**: "Oh no! It’s *Sex and the City*. Make some time for a glam-filled NYC binge session!" - **Visual Add-on**: Include an animated NYC skyline background or glittery graphics for correct answers. #### Question 3: - **Prompt**: "Which celeb famously shaved her head in the 2000s? 👩‍🎤" - **Answer Options**: 1. Britney Spears 2. Christina Aguilera 3. Lindsay Lohan - **Correct Answer**: Britney Spears - **Feedback**: - **Correct**: "👏 Yep, it’s Britney! A legendary moment in pop culture history. 👸🎤" - **Incorrect**: "Not quite! The answer is Britney Spears. That iconic moment is unforgettable!" - **Audio Elements**: Add a drumroll sound for suspense before revealing the answer. #### Final Score Screen: - **Score Tiers**: - **High Score (100%)**: "🎉 You’re a 2000s pop culture master! Share your score and challenge your friends to top it!" - **Medium Score (50-99%)**: "👏 Not bad! You’re almost an expert. Share your score and invite others to play!" - **Low Score (Below 50%)**: "😅 Looks like you need a refresh on 2000s pop culture. Share your score and dare your friends to do better!" - **Gamified Feature**: - Enable the "Challenge a Friend" option in Quizalize to spark competition. - Include a timer extension for players to decrease stress during tricky questions. #### Call-to-Action: - **Prompt**: "How well did you do? Share your results and invite your friends to join the fun! 👇" - **End Message**: "✨ Follow us for more fun quizzes! Who’s ready for the next challenge?" #### Design & Interactive Elements: - **Visual Enhancements**: Use animated GIFs or static pop culture images (e.g., retro TV screenshots, Britney Spears visuals) to visually set the tone for the quiz. - **Audio Elements**: Add celebratory sound effects when revealing correct answers or upon quiz completion. - **Gamified Elements**: Reward streak bonuses for consecutive correct answers to keep players engaged and competitive. This script is tailored for Quizalize, leveraging its gamification features and interactive design options to create a fun and engaging quiz that users will enjoy while fostering friendly competition!

Figure 18-11 represents the amount of energy stored as organic material in each trophic level in an ecosystem. The pyramid shape of the diagram indicates the low percentage of energy transfer from one level to the next. On average, 10 percent of the total energy consumed in one trophic level is incor- porated into the organisms in the next. Why is the percentage of energy transfer so low? One reason is that some of the organisms in a trophic level escape being eaten. They eventually die and become food for decomposers, but the energy contained in their bodies does not pass to a higher trophic level. Even when an organism is eaten, some of the molecules in its body will be in a form that the consumer cannot break down and use. For example, a cougar cannot extract energy from the antlers, hooves, and hair of a deer. Also, the energy used by prey for cellu- lar respiration cannot be used by predators to synthesize new bio- mass. Finally, no transformation or transfer of energy is 100 percent efficient. Every time energy is transformed, such as during the reactions of metabolism, some energy is lost as heat. Limitations of Trophic Levels The low rate of energy transfer between trophic levels explains why ecosystems rarely contain more than a few trophic levels. Because only about 10 percent of the energy available at one trophic level is transferred to the next trophic level, there is not enough energy in the top trophic level to support more levels. Organisms at the lowest trophic level are usually much more abundant than organisms at the highest level. In Africa, for exam- ple, you will see about 1,000 zebras, gazelles, and other herbivores for every lion or leopard you see, and there are far more grasses and shrubs than there are herbivores. Higher trophic levels con- tain less energy, so, they can support fewer individuals.A population is a group of organisms that belong to the same species and live in a particular place at the same time. All of the bass living in a pond during a certain period of time make up a pop- ulation because they are isolated in the pond and do not interact with bass living in other ponds. The boundaries of a population may be imposed by a feature of the environment, such as a lake shore, or they can be arbitrarily chosen to simplify a study of the population. The humans shown in Figure 19-1 are part of the pop- ulation of a city. The properties of populations differ from those of individuals. An individual may be born, it may reproduce, or it may die. A population study focuses on a population as a whole—how many individuals are born, how many die, and so on. Population Size A population’s size is the number of individuals that the population contains. Size is a fundamental and important population property but can be difficult to measure directly. If a population is small and composed of immobile organisms, such as plants, its size can be determined simply by counting individuals. Often, though, individ- uals are too abundant, too widespread, or too mobile to be counted easily, and scientists must estimate the number of individuals in the population. Suppose that a scientist wants to know how many oak trees live in a 10 km2 patch of forest. Instead of searching the entire patch of forest and counting all the oak trees, the scientist could count the trees in a smaller section of the forest, such as a 1 km2 area. The scientist could then use this value to estimate the population of the larger area. SECTION 1 OBJECTIVES ● Describe the main properties that scientists measure when they study populations. ● Compare the three general patterns of population dispersion. ● Identify the measurements used to describe changing populations. ● Compare the three general types of survivorship curves. VOCABULARY population population density dispersion birth rate death rate life expectancy age structure survivorship curve FIGURE 19-1 A population can be widely distributed, as Earth’s human population is, or confined to a small area, as species of fish in a lake are. Copyright © by Holt, Rinehart and Winston. All rights reserved. 382 CHAPTER 19 If the small patch contains 25 oaks, an area 10 times larger would likely contain 10 times as many oak trees. A similar kind of sampling technique might be used to estimate the size of the pop- ulation shown in Figure 19-2. To use this kind of estimate, the sci- entist must assume that the distribution of individuals in the entire population is the same as that in the sampled group. Estimates of population size are based on many such assumptions, so all esti- mates have the potential for error. Population Density Population density measures how crowded a population is. This measurement is always expressed as the number of individuals per unit of area or volume. For example, the population density of humans in the United States is about 30 people per square kilome- ter. Table 19-1 shows the population sizes and densities of humans in several countries in 2003. These estimates are calculated for the total land area. Some areas of a country may be sparsely popu- lated, while other areas are very densely populated. Dispersion A third population property is dispersion (di-SPUHR-zhuhn). Dispersion is the spatial distribution of individuals within the popu- lation. In a clumped distribution, individuals are clustered together. In a uniform distribution, individuals are separated by a fairly con- sistent distance. In a random distribution, each individual’s location is independent of the locations of other individuals in the popula- tion. Figure 19-3 illustrates the three possible patterns of dispersion. Clumped distributions often occur when resources such as food or living space are clumped. Clumped distributions may also occur because of a species’ social behavior, such as when animals gather into herds or flocks. Uniform distributions may result from social behavior in which individuals within the same habitat stay as far away from each other as possible. For example, a bird may locate its nest so as to maximize the distance from the nests of other birds. These migrating wildebeests in East Africa are too numerous and mobile to be counted. Scientists must use sampling methods at several locations to monitor changes in the population size of the animals. FIGURE 19-2 TABLE 19-1 Population Size and Density of Some Countries Population size Population density Country (in millions) (in individuals/km2) China 1,289 135 India 1,069 325 United States 292 30 Russia 146 8 Japan 128 337 Mexico 105 54 Kenya 32 54 Australia 20 3 dispersion from the Latin dis-, meaning “out,” and spargere, meaning “to scatter” Word Roots and Origins Copyright © by Holt, Rinehart and Winston. All rights reserved. POPULATIONS 383 The social interactions of birds called gannets, which are shown in Figure 19-3b, result in a uniform distribution. Each gannet chooses a small nesting area on the coast and defends it from other gannets. In this way, each gannet tries to maximize its distance from all of its neighbors, which causes a uniform distribution of individuals. Few populations are truly randomly dispersed. Rather, they show degrees of clumping or uniformity. The dispersion pattern of a population sometimes depends on the scale at which the popu- lation is observed. The gannets shown in Figure 19-3b are uni- formly distributed on a scale of a few meters. However, if the entire island on which the gannets live is observed, the distribution appears clumped because the birds live only near the shore. POPULATION DYNAMICS All populations are dynamic—they change in size and composition over time. To understand these changes, scientists must know more than the population’s size, density, and dispersion. One important measure is the birth rate, the number of births occur- ring in a period of time. In the United States, for example, there are about 4 million births per year. A second important measure is the death rate, or mortality rate, which is the number of deaths in a

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