Physiology as a tool for at‐risk animal recovery planning: An analysis of Canadian recovery strategies with global recommendations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Many government organizations use recovery planning to synthesize threats, propose management strategies, and determine recovery criteria for threatened wildlife. Little is known about the extent to which physiological knowledge has been used in recovery planning, despite its potential to offer key biological information that could aid in recovery success. Using recovery strategies for at‐risk animal species in Canada as a case study, we analyzed the prevalence, purpose, and type of physiological knowledge being used in recovery planning. We found that 73% of strategies contained mention of physiology and that incorporation of physiology has increased since 2006. Of the various types of physiological tools available, reference to stress, immune, thermal, and bioenergetic metrics appeared most frequently. Physiological information was more likely to be found in the background and threat assessment sections compared to action and future research sections, and less likely to be included in strategies for arthropods and birds compared to other taxonomic groups. By synthesizing our results with previous studies, we provide recommendations to encourage the application of physiological tools in recovery planning worldwide, such as increased incorporation of physiology in ongoing threat monitoring, critical habitat assessments, monitoring the success of recovery actions, and modeling responses to future environmental changes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.000 |
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 it