A conceptual framework for the emerging discipline of conservation physiology
Bibliographic record
Abstract
Current rates of biodiversity decline are unprecedented and largely attributed to anthropogenic influences. Given the scope and magnitude of conservation issues, policy and management interventions must maximize efficiency and efficacy. The relatively new field of conservation physiology reveals the physiological mechanisms associated with population declines, animal-environment relationships and population or species tolerance thresholds, particularly where these relate to anthropogenic factors that necessitate conservation action. We propose a framework that demonstrates an integrative approach between physiology, conservation and policy, where each can inform the design, conduct and implementation of the other. Each junction of the conservation physiology process has the capacity to foster dialogue that contributes to effective implementation, monitoring, assessment and evaluation. This approach enables effective evaluation and implementation of evidence-based conservation policy and management decisions through a process of ongoing refinement, but may require that scientists (from the disciplines of both physiology and conservation) and policy-makers bridge interdisciplinary knowledge gaps. Here, we outline a conceptual framework that can guide and lead developments in conservation physiology, as well as promote innovative research that fosters conservation-motivated policy.
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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 | 0.012 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.004 | 0.041 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".