Principles for the production of evidence‐based guidance for conservation actions
Bibliographic record
Abstract
Abstract Many types of guidance documents inform conservation by providing practical recommendations for the management of species and habitats. To ensure effective decisions are made, such guidance should be based upon relevant and up‐to‐date evidence. We reviewed conservation guidance for mitigation and management of species and habitats in the United Kingdom and Ireland, identifying 301 examples produced by over 50 organizations. Of these, only 29% provided a reference list, of which only 32% provided reference(s) relevant to justify the recommended actions (9% of the total). Furthermore, even this guidance was often outdated, lacked a methodology for production, or did not highlight uncertainty in the key evidence that supported the recommendations. These deficiencies can lead to misguided and ineffective conservation practices, policies, and decisions, and a waste of resources. Based on this review and co‐design by experts from 14 organizations, we present a set of principles for ensuring sufficient and relevant evidence is transparently incorporated into future conservation guidance. Producing evidence‐based guidance in line with these principles would enable more effective conservation outcomes.
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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 | 0.736 | 0.769 |
| Meta-epidemiology (narrow) | 0.006 | 0.010 |
| Meta-epidemiology (broad) | 0.011 | 0.017 |
| Bibliometrics | 0.033 | 0.017 |
| Science and technology studies | 0.010 | 0.034 |
| Scholarly communication | 0.035 | 0.025 |
| Open science | 0.024 | 0.026 |
| Research integrity | 0.046 | 0.048 |
| Insufficient payload (model declined to judge) | 0.004 | 0.005 |
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".