Unequivocal principles for area-based biodiversity conservation
Bibliographic record
Abstract
Recent agreements have strengthened and expanded ongoing international commitments to protect and restore native habitats. Nevertheless, how such commitments should be implemented has been historically controversial, and nuances in ongoing debates are often misunderstood, hindering biodiversity conservation. We propose three unequivocal principles that must be central to how area-based biodiversity conservation will occur in the coming decades. These principles relate to habitat coverage, amount, and connectivity, and their enunciation clarifies apparent contradictions in the literature. We explain why socio-economic considerations that are central to current biodiversity conservation cannot override these principles. Biodiversity must be supported everywhere on Earth, especially when considering the right of human population to access nature and to benefit from countless ecosystem services.
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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 | 0.025 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.041 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.008 | 0.017 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".