Using vitality indicators to predict survival of aquatic animals released from fisheries
Bibliographic record
Abstract
Estimating the survival probability of animals released from fisheries can improve the overall understanding of animal biology with implications for fisheries management, conservation and animal welfare. Vitality indicators are simple visual measures of animal condition that change in response to stressors (like fisheries capture) and can be assessed to predict post-release survival. These indicators typically include immediate reflex responses which are typically combined into a score. Vitality indicators are straight-forward and non-invasive metrics that allow users to quantify how close (or far) an animal is from a normal, 'healthy' or baseline state, which in turn can be correlated with outcomes such as survival probability, given appropriate calibration. The literature on using vitality indicators to predict post-release survival of animals has grown rapidly over the past decade. We identified 136 papers that used vitality indicators in a fisheries context. These studies were primarily focused on marine and freshwater fishes, with a few examples using herptiles and crustaceans. The types of vitality indicators are diverse and sometimes taxa-specific (e.g. pinching leg of turtles, spraying water at nictitating membrane of sharks) with the most commonly used indicators being those that assess escape response or righting response given the vulnerability of animals when those reflexes are impaired. By presenting Pacific salmon fisheries as a case study, we propose a framework for using vitality indicators to predict survival across taxa and fisheries.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 teacher head, 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".