Influence of gear switching on recapture of Atlantic salmon (<i><scp>S</scp>almo salar</i>) in catch‐and‐release fisheries
Bibliographic record
Abstract
Abstract Anglers that release Atlantic salmon ( Salmo salar ) in recreational fisheries do so with the intention that the fish will survive and contribute to succeeding generations. In some instances, salmon that are released may be recaptured, but mechanisms associated with recapture are unclear. To test whether gear avoidance influences recapture rates, we analysed data from tagging programmes in major Norwegian Atlantic salmon fishing rivers to determine how frequently salmon were recaptured by different gear than that by which they were initially captured (i.e. gear switch). Among 339 salmon captured, externally tagged and released in 2012 and 2013, 46 (14%) were recaptured; 70% of these recaptured salmon exhibited gear switch. To test whether this gear switch percentage could be expected in the absence of gear avoidance, a simulation was conducted, which accounted for variation in catch probability among rivers and across time with different gear types based on comprehensive catch data. Each simulation step provided a simulated rate of gear switch under the null hypothesis of no gear avoidance. A distribution was generated, which described the probability that we would observe 70% gear switch. The simulated results indicated that this rate of gear switch was highly unlikely ( P = 0.003) if recapture gear is assumed to be independent of initial capture gear, suggesting that salmon avoided familiar gear types. Changes to behaviour after release, including learned hook avoidance, may explain our observation of gear avoidance by recaptured salmon.
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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 | 0.001 |
| 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".