专题07 阅读理解之说明文(2020-2024年新高考真题)-备战2025年高考英语名校模拟真题速递(新高考专用)

备战2025年高考英语名校模拟真题速递(新高考专用)

第一期

专题07 阅读理解之说明文(2020-2024年新高考真题)

（2024年新高考I卷高考真题）In the race to document the species on Earth before they go extinct, researchers and citizen scientists have collected billions of records. Today, most records of biodiversity are often in the form of photos, videos, and other digital records. Though they are useful for detecting shifts in the number and variety of species in an area, a new Stanford study has found that this type of record is not perfect.

 “With the rise of technology it is easy for people to make observations of different species with the aid of a mobile application,” said Barnabas Daru, who is lead author of the study and assistant professor of biology in the Stanford School of Humanities and Sciences. “These observations now outnumber the primary data that comes from physical specimens (标本), and since we are increasingly using observational data to investigate how species are responding to global change, I wanted to know: Are they usable?”

Using a global dataset of 1.9 billion records of plants, insects, birds, and animals, Daru and his team tested how well these data represent actual global biodiversity patterns.

“We were particularly interested in exploring the aspects of sampling that tend to bias (使有偏差) data, like the greater likelihood of a citizen scientist to take a picture of a flowering plant instead of the grass right next to it,” said Daru.

Their study revealed that the large number of observation-only records did not lead to better global coverage. Moreover, these data are biased and favor certain regions, time periods, and species. This makes sense because the people who get observational biodiversity data on mobile devices are often citizen scientists recording their encounters with species in areas nearby. These data are also biased toward certain species with attractive or eye-catching features.

What can we do with the imperfect datasets of biodiversity?

“Quite a lot,” Daru explained. “Biodiversity apps can use our study results to inform users of oversampled areas and lead them to places — and even species — that are not well-sampled. To improve the quality of observational data, biodiversity apps can also encourage users to have an expert confirm the identification of their uploaded image.”

1．What do we know about the records of species collected now?

A．They are becoming outdated. B．They are mostly in electronic form.

C．They are limited in number. D．They are used for public exhibition.

2．What does Daru’s study focus on?

A．Threatened species. B．Physical specimens.

C．Observational data. D．Mobile applications.

3．What has led to the biases according to the study?

A．Mistakes in data analysis. B．Poor quality of uploaded pictures.

C．Improper way of sampling. D．Unreliable data collection devices.

4．What is Daru’s suggestion for biodiversity apps?

A．Review data from certain areas. B．Hire experts to check the records.

C．Confirm the identity of the users. D．Give guidance to citizen scientists.

 

（2024年新高考II卷高考真题）We all know fresh is best when it comes to food. However, most produce at the store went through weeks of travel and covered hundreds of miles before reaching the table. While farmer’s markets are a solid choice to reduce the journey, Babylon Micro-Farm (BMF) shortens it even more.

BMF is an indoor garden system. It can be set up for a family. Additionally, it could serve a larger audience such as a hospital, restaurant or school. The innovative design requires little effort to achieve a reliable weekly supply of fresh greens.

Specifically, it’s a farm that relies on new technology. By connecting through the Cloud, BMF is remotely monitored. Also, there is a convenient app that provides growing data in real time. Because the system is automated, it significantly reduces the amount of water needed to grow plants. Rather than watering rows of soil, the system provides just the right amount to each plant. After harvest, users simply replace the plants with a new pre-seeded pod (容器) to get the next growth cycle started.

Moreover, having a system in the same building where it’s eaten means zero emissions (排放) from transporting plants from soil to salad. In addition, there’s no need for pesticides and other chemicals that pollute traditional farms and the surrounding environment.

BMF employees live out sustainability in their everyday lives. About half of them walk or bike to work. Inside the office, they encourage recycling and waste reduction by limiting garbage cans and avoiding single-use plastic. “We are passionate about reducing waste, carbon and chemicals in our environment,” said a BMF employee.

5．What can be learned about BMF from paragraph 1?

A．It guarantees the variety of food. B．It requires day-to-day care.

C．It cuts the farm-to-table distance. D．It relies on farmer’s markets.

6．What information does the convenient app offer?

A．Real-time weather changes. B．Current condition of the plants.

C．Chemical pollutants in the soil. D．Availability of pre-seeded pods.

7．What can be concluded about BMF employees?

A．They have a great passion for sports.

B．They are devoted to community service.

C．They are fond of sharing daily experiences.

D．They have a strong environmental awareness.

8．What does the text mainly talk about?

A．BMF’s major strengths. B．BMF’s general management.

C．BMF’s global influence. D．BMF’s technical standards.

 

（2023年新高考I卷高考真题）On March 7, 1907, the English statistician Francis Galton published a paper which illustrated what has come to be known as the “wisdom of crowds” effect. The experiment of estimation he conducted showed that in some cases, the average of a large number of independent estimates could be quite accurate.

This effect capitalizes on the fact that when people make errors, those errors aren’t always the same. Some people will tend to overestimate, and some to underestimate. When enough of these errors are averaged together, they cancel each other out, resulting in a more accurate estimate. If people are similar and tend to make the same errors, then their errors won’t cancel each other out. In more technical terms, the wisdom of crowds requires that people’s estimates be independent. If for whatever reasons, people’s errors become correlated or dependent, the accuracy of the estimate will go down.

But a new study led by Joaquin Navajas offered an interesting twist (转折) on this classic phenomenon. The key finding of the study was that when crowds were further divided into smaller groups that were allowed to have a discussion, the averages from these groups were more accurate than those from an equal number of independent individuals. For instance, the average obtained from the estimates of four discussion groups of five was significantly more accurate than the average obtained from 20 independent individuals.

In a follow-up study with 100 university students, the researchers tried to get a better sense of what the