Elevating the impact of conservation physiology by building a community devoted to excellence, transparency, ethics, integrity and mutual respect
Bibliographic record
Abstract
Much has been accomplished since 2012 including publishing over 600 papers in the journal and helping to build a sense of place for aspiring and practicing conservation physiologists (Cooke et al., 2020).Yet, more work is needed to further elevate the impact of conservation physiology as a discipline and community.Here, we summarize what is needed to build and strengthen a community devoted to not only excellence, transparency, ethics, integrity and mutual respect, but also courage to tackle some of the overarching challenges humanity faces.As active voices in the conservation physiology community we hope that this paper will help shape the future of our discipline while also guiding the activities and priorities of the journal and editorial team.Since the term 'conservation physiology' was coined by Wikelski and Cooke (2006) it has emerged as an essential component of conservation science and practice.Conservation physiology is about the use of physiological tools, knowledge and concepts to understand and solve conservation problems across diverse taxa (Cooke et al., 2013).It is regarded as being particularly effective at understanding mechanisms, generating cause-effect relationships (e.g.threat X does Y to organism Z), creating predictive tools and testing conservation interventions (Cooke and O'Connor, 2010).Issues relevant to conservation physiology range from
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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 | 0.014 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.025 | 0.012 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.017 | 0.010 |
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".