Catch-and-release of Atlantic cod (<i>Gadus morhua</i>): post-release behaviour of acoustically pretagged fish in a natural marine environment
Bibliographic record
Abstract
Studying the sublethal effects of catch-and-release (C&R) is challenging, as there are several potential sources of bias. For example, if behavioural alterations immediately after the release event are to be studied, separation of tagging effects from actual C&R effects is required, which is a challenge in the wild, particularly in marine environments. To investigate the effects of C&R on Atlantic cod (Gadus morhua) in their natural environment, 80 cod were caught in fyke nets, fitted with acoustic transmitters, and released. After recovery from tagging and handling for at least 14 days, nine individuals were recaptured and released at least once during experimental angling, following best release practice. All cod survived the C&R event and did not show any large-scale behavioural changes (i.e., changes in diel vertical migrations). However, analysis of small-scale vertical movements showed that three individuals underwent short-term alterations (e.g., reduced or increased swimming activity). This study showed that pretagging fish with acoustic transmitters before experimental angling is an option when investigating fish behaviour immediately after the release event in marine environments. Moreover, release guidelines for cod should be developed, as cod can recover quickly if caught in shallow waters (<20 m) and properly handled and released.
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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 | 0.000 | 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 | 0.000 | 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".