Biodiversity in the boardroom: a guide to nature-related data for companies, investors, and their legal advisors
Bibliographic record
Abstract
Abstract The aim of this article is to equip legal professionals with an understanding of the landscape of nature and biodiversity data relevant to companies and investors, explaining which data types are applicable in which contexts, and linking this to the requirements of standards and regulations involving nature-related data disclosure. This article focuses on nature and biodiversity data that are particularly relevant to business leaders at multinational companies and financial institutions, and the legal professionals who advise these entities. The article examines two core types of nature and biodiversity metrics—pressures on nature and state of nature. Through this lens, the article unpacks the use cases of each type of metric, taking into account its origin, reliability, and practical applications. It goes on to explore the emerging landscape of nature-related standards and regulations through a case study, looking specifically at the European Union’s Corporate Sustainability Reporting Directive’s nature and biodiversity data requirements. Readers will gain the context needed to navigate nature and biodiversity data effectively, equipping them to better guide companies and investors on nature and biodiversity-related disclosure and decision-making. The article assumes a high-level understanding of corporate environmental practices, and some contextual knowledge of the Environmental, Social, and Governance reporting landscape and key global frameworks (e.g. United Nations Convention for Biological Diversity) but no prior knowledge of nature and biodiversity metrics is required.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.047 | 0.033 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".