Validation of reflex indicators for measuring vitality and predicting the delayed mortality of wild coho salmon bycatch released from fishing gears
Bibliographic record
Abstract
Summary 1. Effective management of fish and wildlife populations benefits from an understanding of the effects of stressors on individual physiology. While physiological knowledge can provide a mechanistic understanding of organismal responses, its applied utility is limited because it cannot easily be used by stakeholders. 2. Reflex action mortality predictors (RAMP) is a method that involves checking for the presence or absence of natural animal reflexes to generate a condition (RAMP) score in response to stressors and to predict fate. The method has previously been validated with fishes in artificial laboratory‐ and field‐based holding studies as a responsive measure of fisheries capture stress and a predictor of delayed mortality, but has not been evaluated in the wild. 3. We used radio telemetry to monitor migration success of 50 endangered coho salmon Oncorhynchus kisutch following incidental capture in an aboriginal beach seine fishery in the lower Fraser River (Canada). RAMP was used to measure the condition of fish at release and to predict migration success following capture. Biopsy of an additional 43 coho profiled physiological condition at time of release. 4. Individuals with greater reflex impairment (higher RAMP scores) at release experienced significantly higher rates of migration failure. RAMP scores were also significantly correlated with fishery handling time. Plasma variables showed that captured coho had experienced physiological stress characteristic of exhaustive exercise and hypoxia, with significantly elevated cortisol and lactate values for fish entangled longer in fishing gear. 5. Synthesis and applications. This is the first validation of RAMP in a wild setting. Based on our findings, fishers could use the method and make adjustments in fishing behaviour in real‐time to improve fish condition and reduce the mortality of bycatch, and conservation practitioners could monitor animal condition and identify problems that deserve management attention. RAMP is an easy, rapid and inexpensive approach to predicting mortality and measuring vitality and performed better than traditional physiological tools that cannot easily be used by stakeholders.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".