What is a database? A database is a collection of data or information that is organized, stored, and accessed electronically.
It is relevant (相关的). When searching for information in the database, you should be able to get results relevant to you. There are several ways to search through a database, for example, using keywords, subject headings, terminology (术语)，etc.
It is accessible. First of all, access to the library is either free or bought by supporters so that you can access the information within it at no price.
| A．It is reliable. |
| B．It is reasonable. |
| C．You may have to pay a small amount of money as an independent researcher. |
| D．Google Scholar isn't a database using the above three aspects as a determining factor. |
| E．It is organized so that searching and finding this information is easy and rapid. |
| F．They are all relevant factors to be referred to when searching educational databases. |
| G．So you can limit your search results to a certain source type or date, or you can search by author or title. |
相似题推荐
【推荐1】We Need to Think about Conservation on a Different Timescale
Time, perceived by humans in days, months, and years, contrasts with nature’s grander scales of centuries and millennia, referred to as “deep time.” While paleontologists (古生物学者) are trained to think in deep time, conservationists are realizing the challenges it poses. Shortsightedness about time limits modern conservation, with efforts often overlooking past healthy conditions of ecosystems in the context of climate and biodiversity crises.
The shifting baseline syndrome (综合症), where standards in a place change gradually, makes conservation more complex. It involves evaluating ecosystems primarily on their recent past, often with negative consequences.
Recent shifts in California’s forest management practices, from stopping fires to embracing Indigenous knowledge of controlled burns, exemplify the importance of understanding historical ecosystem dynamics. To enhance conservation, adopting a deep-time approach is crucial.
Modern mathematical modeling, combined with long-term data, offers a pathway for preserving ecosystems. In California’s kelp (海带、海藻) forest, researchers identified an overlooked keystone species — the extinct Seller’s Sea Cow (大海牛). By examining past kelp forests, a deeper story impacting regeneration was revealed. The sea cow, a massive plant-cater, contributed to a diverse, vital undergrowth by trimming kelp and letting light reach the area.
The researchers put forward a novel approach to kelp forest restoration: selectively harvesting kelp, imitating the sea cow’s impact. This strategy, considering historical dynamics, challenges assumptions about recent ecosystems and offers new conservation methods.
Rather than only focusing on removing urchins (海胆) or reintroducing sea otters, the researchers suggest employing teams of humans to selectively harvest kelp, as the Steller’s sea cow once did, to encourage fresh growth. This sustainable harvest could benefit both the ecosystem and human consumption.
In short, assumptions based on the recent past may impede the understanding and protection of ecosystems. On the other hand, the application of controlled burns, similar modeling studies, and a deep-time perspective (视角) could significantly transform conservation efforts. Recognizing our role in an ongoing narrative spanning millions of years is essential, urging a comprehensive understanding of ecosystems through time. Embracing this role is crucial for shaping the future and establishing vital connections from the past to the future.
1. What is the “shifting baseline syndrome,” mentioned in the passage?| A．A syndrome that affects human beings’ perception of time. |
| B．A phenomenon where ecological standards shift in a place. |
| C．A psychological disorder common among conservationists. |
| D．A condition where ecosystems change gradually over time. |
| A．It promotes the prevention of wildfires. | B．It aids in mathematical modeling efforts. |
| C．It helps reveal historical ecosystem dynamics. | D．It enhances human consumption of ecosystems. |
| A．Reform. | B．Disrupt. | C．Quicken. | D．Deepen. |
| A．Shifting baseline syndrome has positive ecological changes. |
| B．Mathematical modeling with the latest data can be effective. |
| C．Deep-time perspective and historical dynamics are crucial. |
| D．Recent history is more preferred in ecosystem restoration. |
【推荐2】We buy because it makes us happy. For some people, if they're feeling blue, shopping will make them happier because it restores some control in their lives.It's making the choice to buy or not to buy that helps people feel more in control.Retail therapy proves to be a useful and practical thing.
Doing something we find rewarding lights up the brain's pleasure center, which when activated leads to the release of dopamine(多巴胺), which makes us feel good. It's why we can even get addicted to the pleasure of buying. Experiments with the brain's dopamine releases in pleasurable situations have found that expectations also kicks them off to leave us in a joyful atmosphere; we experience pleasure, in other words, when we're looking forward to a fun event as well as during and after it. A trip to the mall is also enjoyable in our schedules for the weekend.
Pleasure is not the only reason why we buy things we don't need at all. Ryan Howell, an professor of psychology at San Francisco State University said back in our hunter and gatherer days，when people saw something they wanted, they'd grab it, even if they didn't need it, because it was likely they wouldn't come across that item again. “If you see something that seems to be running in short supply，you're going to get it,” Howell said. These days, such scarcity isn't an issue — we can buy nearly anything we want if we have the means — but we often still approach life like our ancestors did, especially when it comes to a sale. When we see a 50% off clearance price tag, that scarcity impulse kicks into gear, Howell said. The feeling is, if we don't buy that item now, it's going to be gone forever — or at least at that good price.
1. What can we learn from the first paragraph?| A．People may lose control of life occasionally. |
| B．People tend to be controlled by purchasing desire. |
| C．Purchase can comfort people when they are upset. |
| D．The concept of retail therapy was once a false idea. |
| A．Experiments. | B．Expectations. |
| C．Dopamine releases. | D．Pleasurable situations. |
| A．A good bargain can’t be missed. |
| B．Our ancestors grabbed things for survival. |
| C．We should watch out for the trap of the sale. |
| D．Our ancestors had similar attitude with us to things wanted. |
| A．Why We Buy Things |
| B．To Purchase As You Wish |
| C．The Approach to Happiness |
| D．The Relaxation in Modern Life |
【推荐3】Walk through the Amazon rainforest today and you will find it is steamy, warm, damp and thick. But if you had been around 15,000 years ago, during the last ice age, would it have been the same? For more than 30 years, scientists have been arguing about how rainforests like the Amazon might have reacted to the cold, dry climates of the ice ages, but until now, no one has reached a satisfying answer.
Rainforests like the Amazon are important for mopping up CO2 from the atmosphere and helping to slow global warming. Currently the trees in the Amazon take in around 500 million tons of CO2 each year: equal to the total amount of CO2 giving off in the UK each year. But how will the Amazon react to future climate change? If it gets drier, will it still survive and continue to draw down CO2 ?
Scientists hope that they will be able to learn in advance how the rainforest will manage in the future by understanding how rainforests reacted to climate change in the past. Unfortunately, getting into the Amazon rainforest and collecting information are very difficult. To study past climate, scientists need to look at fossilized pollen, kept in lake mud. Going back to the last ice age means drilling deep down into lake sediments （沉淀物）which requires specialized equipment and heavy machinery. There are very few roads and paths, or places to land helicopters and aeroplanes. Rivers tend to be the easiest way to enter the forest, but this still leaves vast areas between the rivers completely unsampled（未取样）.So far, only a handful of cores have been drilled that go back to the last ice age and none of them provide enough information to prove how the Amazon rainforest reacts to climate change.
1. What does the underlined phrase “mopping up” in the second paragraph mean?| A．Giving up. | B．Giving out. |
| C．Wiping out. | D．Taking in. |
| A．It’ll get drier and continue to remove CO2 . |
| B．There is no exact answer up to present. |
| C．It’ll get warmer and then colder and drier. |
| D．It’ll remain steamy, warm, damp and thick. |
| A．It’s important to drill deep down into lake sediments to collect information. |
| B．It’s impossible to prove how climate changes in the Amazon rainforest. |
| C．It’s hard to collect information for studies of the past climate in the Amazon rainforest. |
| D．It’s necessary to have specialized equipment and machinery to study the past climate. |
| A．Studies of the Rainforests |
| B．Climates of the Amazon |
| C．Secrets of the Ice Age |
| D．Changes of the Rainforests |
【推荐1】Microsoft announced this week that its facial-recognition system is now more accurate in identifying people of color, touting (吹嘘）its progress at tackling one of the technology’s biggest biases (偏见).
But critics, citing Microsoft’s work with Immigration and Customs Enforcement, quickly seized on how that improved technology might be used. The agency contracts with Microsoft for cloud-computing tools that the tech giant says is largely limited to office work but can also include face recognition.
Columbia University professor Alondra Nelson tweeted, “We must stop confusing ‘inclusion’ in more ‘diverse’ surveillance (监管）systems with justice and equality.”
Facial-recognition systems more often misidentify people of color because of a long-running data problem： The massive sets of facial images they train on skew heavily toward white men. A Massachusetts Institute of Technology study this year of the face-recognition systems designed by Microsoft, IBM and the China-based Face++ found that facial-recognition systems consistently giving the wrong gender for famous women of color including Oprah Winfrey, Serena Williams, Michelle Obama and Shirley Chisholm, the first black female member of Congress.
The companies have responded in recent months by pouring many more photos into the mix, hoping to train the systems to better tell the differences among more than just white faces. IBM said Wednesday it used 1 million facial images, taken from the photo-sharing site Flickr, to build the “world’s largest facial data-set” which it will release publicly for other companies to use.
IBM and Microsoft say that allowed its systems to recognize gender and skin tone with much more precision. Microsoft said its improved system reduced the error rates for darker-skinned men and women by “up to 20 times,” and reduced error rates for all women by nine times.
Those improvements were heralded（宣布）by some for taking aim at the prejudices in a rapidly spreading technology, including potentially reducing the kinds of false positives that could lead police officers misidentify a criminal suspect.
But others suggested that the technology's increasing accuracy could also make it more marketable. The system should be accurate, “but that’s just the beginning, not the end, of their ethical obligation,” said David Robinson, managing director of the think tank Upturn.
At the center of that debate is Microsoft, whose multimillion-dollar contracts with ICE came under fire amid the agency’s separation of migrant parents and children at the Mexican border.
In an open letter to Microsoft chief executive Satya Nadella urging the company to cancel that contract, Microsoft workers pointed to a company blog post in January that said Azure Government would help ICE “accelerate recognition and identification.” “We believe that Microsoft must take an ethical stand, and put children and families above profits,” the letter said.
A Microsoft spokesman, pointing to a statement last week from Nadella, said the company’s “current cloud engagement” with ICE supports relatively anodyne（温和的）office work such as “mail, calendar, massaging and document management workloads.” The company said in a statement that its facial-recognition improvements are “part of our going work to address the industry-wide and societal issues on bias.”
Criticism of face recognition will probably expand as the technology finds its way into more arenas, including airports, stores and schools. The Orlando police department said this week that it would not renew its use of Amazon. com’s Rekognition system.
Companies ”have to acknowledge their moral involvement in the downstream use of their technology,”
Robinson said. “The impulse is that they’re going to put a product out there and wash their hands of the consequences. That’s unacceptable.”
1. What is “one of the technology’s biggest biases” in Paragraph 1?| A．Class bias. | B．Regional difference. |
| C．Professional prejudice. | D．Racial discrimination. |
| A．Justice and equality have been truly achieved. |
| B．It is due to the expansion of the photo database. |
| C．It has already solved all the social issues on biases. |
| D．The separation of immigrant parents from their children can be avoided. |
| A．Data problems. | B．The market value. |
| C．The application field. | D．A moral issue |
| A．Skeptical. | B．Approval. |
| C．Optimistic. | D．Neutral. |
| A．companies had better hide from responsibilities |
| B．companies deny problems with its technical process |
| C．companies should not launch new products on impulse |
| D．companies should be responsible for the new product and the consequences |
| A．The wide use of Microsoft system | B．Fears of facial-recognition technology |
| C．The improvement of Microsoft system | D．Failure of recognizing black women |
What may well be the oldest metal coins in the world have been identified at an ancient abandoned city known as Guanzhuang in China. Like many Bronze Age (青铜时代) coins from the region, they were cast in the shape of spades with finely carved handles. These ancient coins existed during an in-between period between barter (以物易物) and money, when coins were a novel concept, but everybody knew that agricultural tools were valuable.
Reading about this incredible discovery, I kept thinking about the way modern people represent computer networks by describing machines as having “addresses”, like a house. We also talk about one computer using a “port” to send information to another computer, as if the data were a floating boat with destination. It’s as if we are in the Bronze Age of information technology, grasping desperately for real-world reference to transform our civilization.
Now consider what happened to spade coins. Over centuries, metalworkers made these coins into more abstract shapes. Some became almost human figures. Others’ handles were reduced to small half-circles. As spade coins grew more abstract, people carved them with number values and the locations where they were made. They became more like modern coins, flat and covered in writing. Looking at one of these later pieces, you would have no idea that they were once intended to look like a spade.
This makes me wonder if we will develop an entirely new set of symbols that allow us to interact with our digital information more smoothly.
Taking spade coins as our guide, we can guess that far-future computer networks will no longer contain any recognizable references to houses. But they still might bring some of the ideas we associate with home to our mind. In fact, computer networks — if they still exist at all — are likely to be almost the indispensable part of our houses and cities, their sensors inset with walls and roads. Our network addresses might actually be the same as our street addresses. If climate change leads to floods, our mobile devices might look more like boats than phones, assisting us to land.
My point is that the metaphors of the information age aren’t random. Mobile devices do offer us comfort after a long day at work. In some sense, our desire to settle on the shores of data lakes could change the way we understand home, as well as how we build computers. So as we cast our minds forward, we have to think about what new abstractions will go along with our information technology. Perhaps the one thing we count on is that humans will still appreciate the comforts of home.
1. Many Bronze Age coins were made into the shape of a spade because ___________.| A．a lot of emphasis was put on agriculture |
| B．this stylish design made the coins valuable |
| C．these coins also served as agricultural tools |
| D．the handles made the coins easily exchanged |
| A．To show they both used to be new concepts when first invented. |
| B．To explain abstract digital worlds are different from concrete coins. |
| C．To suggest computers will experience dramatic changes as coins did. |
| D．To highlight their same importance in our civilizational transformation. |
| A．Flexible. | B．Essential. | C．Wasteful. | D．Alternative. |
| A．What Coins and Computers Bring Us |
| B．How Agriculture Loses to Digital Industry |
| C．How Bronze Age Develops to Information Age |
| D．What Ancient Money Tells Us About the Future |
【推荐3】Artificial—intelligence systems like Grammarly, an automated grammar—checker, are trained with data. for instance, translation software is fed sentences translated by humans, Grammarly's training data involve a large number of standard error—free sentences and human—corrected sentences.
But grammar is the real magic of language, joining words into structures, joining those structures into sentences, and doing so in a way that maps onto meaning.
| A．Grammarly can seem to miss more errors than it marks. |
| B．One Grammarly feature that works fairly well is feeing analysis. |
| C．To correct such writing requires knowing what the writer intended. |
| D．Grammarly has some obvious strengths in understanding meaning or intentions. |
| E．Computers outpace humans at problems that can be solved with pure maths. |
| F．Developers also add certain rules to the patterns Grammarty has taught itself. |
| G．In this decisive structure—meaning connection, machines are no match for humans. |
