Behaviour and survival of wild Atlantic salmon Salmo salar captured and released while surveillance angling for escaped farmed salmon
Bibliographic record
Abstract
In many Norwegian rivers, spawning stocks are surveyed for escaped farmed salmon with surveillance fishing by rod and reel after the recreational angling season.However, the benefits of surveillance fishing depend on the ability of wild salmon to return to the spawning stock.To evaluate the impacts of surveillance fishing, we captured, radio-tagged and released wild Atlantic salmon Salmo salar in the River Lakselva, Norway, in a surveillance fishery occurring just prior to the spawning period.Among 39 salmon captured, 36 wild fish were tagged and released, whereas 3 were not released (1 bleeding from the gills, 1 farmed, 1 farmed and bleeding).Survival of fish captured by surveillance fishing was high (95% total survival, 100% catch-and-release survival).Tagged fish were tracked on average 1.2 ± 2.8 (SD) km from the release site at the end of the experiment during the spawning season, not significantly different from the distance moved by salmon radio tagged throughout the summer during a similar interval (15 September to 24 October 2014).Total movement within 3 d of release was inferred to average 1.9 ± 2.1 km, excluding 1 individual that exited the river.Tracking data revealed an immediate behavioural reaction of salmon to surveillance catch-and-release angling, the long-term consequences of which are uncertain.Surveillance fishing may be problematic in rivers with small and vulnerable wild stocks in which a high proportion of the spawning populations is sampled.Surveillance fishing completed with ample time before spawning would be a precautious approach to minimize potential effects during spawning.
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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".