Towards positive net outcomes for biodiversity, and developing safeguards to accompany headline biodiversity indicators
Bibliographic record
Abstract
Achieving the Global Biodiversity Framework will necessitate whole production systems contributing towards 'halting and reversing' net biodiversity loss, counterbalancing negative impacts with comparable gains. Here, we report on an illustrative quantitative exploration into the feasibility of monitoring for positive net biodiversity outcomes for the Dutch dairy production sector, using a composite metric. We analysed performance data from 8,950 dairy farms across the Netherlands, combining these data into an integrated biodiversity index. Usefully, this index allowed us to calculate sectoral baseline biodiversity impacts, and explore possible biodiversity strategies. We show that the largest overall source of impacts is imported feed; interestingly, nutrient loads contribute little to the footprint, despite representing an important political issue nationally. This highlights a general risk in using single indices to track net biodiversity outcomes: that they could result in an exclusionary focus, and perverse outcomes. Consequently, we develop safeguards to accompany the index; showing the necessity of incorporating safeguards, but also that meeting them could reduce sectoral biodiversity impacts by ~94%. Our proposed strategies vary in feasibility, all requiring trade-offs between biodiversity, land availability, and production.
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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 | 0.017 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".