同学们好，我是你们的英语助教考小助
今天带大家手撕【24年高考全国I卷英语阅读D篇】。这篇探讨“生物打卡数据偏差”的科研文，看似贴近生活，实则长难句密集、逻辑嵌套深！很多同学读完只记得“拍照”，做题时直接掉坑。废话不多说，咱们直接开拆！
以下原题可以滑动查看哦
D
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.”
32. 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A. They are becoming outdated.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.
33. What does Daru’s study focus on?
A. Threatened species.
B. Physical specimens.
C. Observational data.
D. Mobile applications.
34. 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.
35. 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.
第一关
阅读障碍粉碎机
这篇文章最大的痛点在于，作者为了表达科研的严谨性，使用了大量的“多重从句嵌套”和“长介词短语”。考小助为你挑出了最容易卡壳的两大难句，咱们层层剥开！
难句一（第二段）
“These observations now outnumber the primary data that comes from physical specimens, and since we are increasingly using observational data to investigate how species are responding to global change, I wanted to know: Are they usable?”
难点在哪里？
这句话长达45个单词，融合了并列句、定语从句、原因状语从句和宾语从句！多重逻辑交织在一起，读到最后很容易忘了开头在讲什么。
结构层层剥：
1. 第一层主句：These observations now outnumber the primary data（这些观测数据现在超过了原始数据）。
2. 修饰 primary data 的定语从句：that comes from physical specimens（来自实体标本的原始数据）。
3. and 连接的第二层逻辑（原因状语从句）：since we are increasingly using observational data to investigate...（既然我们越来越多地使用观测数据来调查……）。
4. investigate 的宾语从句：how species are responding to global change（物种如何应对全球变化）。
5. 最终的主句与疑问：I wanted to know: Are they usable?（我想知道：它们可用吗？）。
这句话在说什么：
大众拍照数据超过了实体标本数据 -> 既然我们在用它搞研究 -> 那它到底靠谱吗？
难句二（第四段）
“We were particularly interested in exploring the aspects of sampling that tend to bias 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.
难点在哪里？
主干很短，但修饰成分极长！特别是 like 后面跟了一个结构非常臃肿的举例（包含名词所有格意味的of结构和不定式），容易让人产生强烈的阅读疲劳，抓不住重点。
结构层层剥：
1. 主干框架：We were particularly interested in exploring the aspects of sampling（我们特别有兴趣探索采样的各个方面）。
2. 修饰 aspects 的定语从句：that tend to bias data（那些容易使数据产生偏差的方面）。
3. like 引导的超长举例：like the greater likelihood of a citizen scientist to take a picture...（比如公民科学家更有可能去拍……）。
4. instead of 对比结构：of a flowering plant instead of the grass right next to it（拍开花的植物，而不是旁边的草）。
这句话在说什么：
我们想研究数据为什么会有偏差 -> 比如大家只爱拍好看的花 -> 却不拍旁边的野草。
第二关
出题套路大起底
这篇阅读最具杀伤力的是对“数据为何产生偏差”的细节推理考查。咱们来看看命题人是如何设置干扰项，对不求甚解的同学进行“降维打击”的。
题目：
34. What has led to the biases according to the study?（根据这项研究，是什么导致了偏差？）
考查点定位：
细节理解与推理判断（定位至第四、五段）。
干扰项陷阱分析：
【错误】选项 A. Mistakes in data analysis.
o 陷阱手法：无中生有/偷换概念。 原文通篇讨论的是数据“收集（collection/sampling）”阶段本身就带有偏好（只拍好看的），导致数据源头就不客观。文章根本没有提到科研人员在“分析（analysis）”这些数据时犯了错误。出题人故意用科研词汇来忽悠你。
【错误】选项 B. Poor quality of uploaded pictures.
o 陷阱手法：张冠李戴。 原文最后一段确实提到了“uploaded image（上传的图像）”，并建议专家来确认。但专家确认的是“物种身份（identification）”，而不是因为图片“画质差（poor quality）”。导致偏差的核心原因是“拍了什么”，而不是“拍得清不清晰”。
【正确】选项 C. Improper way of sampling.
o 破局逻辑：回文定位到第四段：“We were particularly interested in exploring the aspects of sampling that tend to bias data…”（我们特别有兴趣探索容易使数据产生偏差的采样方面）。第五段紧接着解释了这种“不恰当的采样方式”具体是什么：人们偏好某些地区、某些时间段，并且只喜欢拍“有吸引力或引人注目的物种（attractive or eye-catching features）”。C选项是对原文“aspects of sampling”的高度同义替换与概括。
【错误】选项 D. Unreliable data collection devices.
o 陷阱手法：偷换概念。 原文第五段提到了“mobile devices（移动设备）”，但文章的意思是“人们用移动设备去记录身边遇到的物种”，并没有说手机或APP这些设备本身“不可靠（unreliable）”。产生偏差的是“使用设备的人（公民科学家）”的偏好，而不是设备出了故障。
更多题目解析可登录【考神·一题一课】查看，每道题都配有选项分析哦~
第三关
高阶阅读提升策略
针对这类“披着科普外衣，实则考查逻辑严密性”的科研说明文，高三的同学们在日常备考中必须完成以下思维升级：
1. 阅读策略：像雷达一样抓取“科研转折词与设问句”
读科技文，第一遍绝对不要沉溺于生僻的动植物名词！你要重点圈画：
转折信号：如第一段末尾的 Though they are useful... this type of record is not perfect.。看到 not perfect，立刻警惕起来——本文的核心探讨点来了：它不完美在哪？怎么解决？
设问结构：倒数第二段 What can we do with the imperfect datasets...? 这种自问自答的结构，就是文章从“提出问题/分析问题”向“解决问题”过渡的绝对路标，往往对应着最后一道大题。
2. 思维训练：如何精准捕捉“作者/专家的隐含态度”？
很多同学面对“不完美的数据（imperfect datasets）”，容易主观臆断作者是“批评/否定”的态度。
· 抓最后一段的 Expert Quote（专家引言）：面对不完美的数据我们能做什么？最后一段专家立刻回答 “Quite a lot”（能做的很多），并给出了利用APP引导用户去采样不足的地区、找专家鉴定等解决方案。这说明作者和专家的态度是客观积极（Objective & Constructive）的，是在寻找解决之道，而非一味贬低。日常练习中，一定要通过把握末段的动词（如 improve, encourage）来定调态度。
3. 考小助特别建议：警惕科研文中的“概念替换”陷阱
高考科技文极其喜欢把“现象、原因、解决方案”里的名词混搭在一起作为干扰项。比如上面第34题，把“采样问题”偷换成“分析问题”或“设备问题”。建议大家在读文章时，顺手在草稿纸上画一个极简的逻辑箭头（如：人爱拍好看的花 -> 采样有偏好 -> 数据不全面 -> APP引导/专家鉴定 -> 解决）。有了这个骨架，任何张冠李戴的选项你都能一眼识破！
今天的考小助课堂同学们是否有所收获呢？后续会继续更新23-25年的全国Ⅰ、Ⅱ卷高考真题解析系列内容，大家记得持续关注哦！
往期精彩，不容错过！点击链接直达↓
【山东·必看真题】26届山东实验中学高三1月测试英语阅读D篇通关攻略
点击“阅读原文”
传送至【考神·一题一课】平台
