A synthesis to understand responses to capture stressors among fish discarded from commercial fisheries and options for mitigating their severity
Bibliographic record
Abstract
Abstract Discarding non‐target fish from commercial fisheries is controversial and has been a persistent concern for fisheries managers globally. Discard management strategies typically begin by understanding mortality rates among discarded fish, a challenging task given the dynamic, highly context‐specific nature of fisheries. An alternative is to develop our knowledge of how stressors operate by first understanding the causes of mortality that drive this context dependence. Particularly relevant to mitigation efforts is an understanding of how fish respond to the physical factors of fishing, such as the gear itself and methods of fishing and handling the gear. We provide a synthesis of how commercial fishing methods may influence discard mortality and outline means by which capture‐induced stress and injury can be mitigated for common commercial gear types, emphasizing method variants or alternatives during capture, handling, and release that could improve survival. This synthesis identifies exhaustion and injury as the most detrimental and ubiquitous stressors experienced by discarded fish, with few options for mitigating their effects. Trawls and hanging net fisheries are identified as the most harmful gears for by‐catch, characterized by high stress regardless of method variants and limited options for mitigation. Irrespective of gear type and type of stressor, minimizing durations of capture and handling and encouragement of good handling behaviour (e.g., during landing and sorting) will reduce the magnitude of stress and injury in fish, and ultimately increase survival.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".