Tailoring Global Data to Guide Corporate Investments in Biodiversity, Environmental Assessments and Sustainability
Bibliographic record
Abstract
Companies make significant investments in environmental impacts assessments, biodiversity action plans, life-cycle assessments, and environmental management systems, but guidance on where and when these tools can be best used, and how they may scale-up to inform corporation-wide planning, is sorely lacking. A major barrier to informed environmental decision-making within companies, especially in data poor regions of the world, is the difficulty accessing, analyzing, and interpreting biodiversity information. To address this shortcoming, we analyzed nine publicly available environmental datasets, and created five globally-relevant metrics associated with biodiversity: habitat intactness, habitat protection, species richness (globally and biome normalized), and threatened species. We demonstrate how packaging these metrics within an open-source, web-based mapping tool can facilitate corporations in biodiversity prioritization of their sites (or their supply chains), ultimately guiding potential investments in the environment.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".