Guide to use public biodiversity data in the private sector
Bibliographic record
Abstract
Nature and biodiversity underpin our economy and society, yet businesses and financial institutions still face challenges in integrating biodiversity considerations into decision-making. This report explores the critical role of public biodiversity and nature-related data in supporting corporate action, risk assessment, and regulatory compliance, particularly in the context of the Kunming-Montreal Global Biodiversity Framework and the TNFD recommendations. It provides an overview of the current landscape of publicly available biodiversity data, identifies key barriers to its use (including fragmentation, licensing issues, and limited ecological literacy) and offers practical examples and recommendations for companies and financial institutions. While recognising the importance of internal data, the focus is on external datasets produced by public bodies and the scientific community. The report outlines two mutually reinforcing priorities: Businesses must begin using existing data to build internal capability and demand. Systemic support is needed to improve data quality, accessibility, and relevance. It calls for collaboration across sectors and highlights the shared responsibility of financing biodiversity data infrastructure. Ultimately, the report aims to accelerate the use of public data to support nature-positive outcomes and corporate accountability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.012 | 0.026 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.079 | 0.043 |
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; both teacher heads agree on what is shown here.
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".