Catch‐and‐release science and its application to conservation and management of recreational fisheries
Bibliographic record
Abstract
Abstract Catch‐and‐release angling is a well‐established practice in recreational angler behaviour and fisheries management. Accompanying this is a growing body of catch‐and‐release research that can be applied to reduce injury, mortality and sublethal alterations in behaviour and physiology. Here, the status of catch‐and‐release research from a symposium on the topic is summarised. Several general themes emerged including the need to: (1) better connect sublethal assessments to population‐level processes; (2) enhance understanding of the variation in fish, fishing practices and gear and their role in catch and release; (3) better understand animal welfare issues related to catch and release; (4) increase the exchange of information on fishing‐induced stress, injury and mortality between the recreational and commercial fishing sectors; and (5) improve procedures for measuring and understanding the effect of catch‐and‐release angling. Through design of better catch‐and‐release studies, strategies could be developed to further minimise stress, injury and mortality arising from catch‐and‐release angling. These strategies, when integrated with other fish population and fishery characteristics, can be used by anglers and managers to sustain or enhance recreational fishing resources.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".