The effect of trapping on the migration and survival of Atlantic salmon smolts
Bibliographic record
Abstract
Abstract Electronic tags are often used to track the freshwater‐marine migrations of smolts, where smolts are captured for tagging pre‐migration (e.g., via electrofishing) or during‐migration (e.g., via traps). Pre‐migration capture allows smolts to initiate and complete their downstream migration unhindered, but risks smolt loss before the migration commences. The contrary is the case for during‐migration trap‐caught smolts, but trapping smolts temporarily halts their seaward journey which may negatively impact their progress. This study investigated the effect of trapping on the behaviour and survival of migrating Atlantic salmon (Salmo Salar) smolts using acoustic telemetry. We compared the movements and survival of smolts tagged before the smolt run captured by electrofishing (“comparator”) with smolts trapped and tagged during the smolt run (“trapped”). A total of 478 smolts were tagged and released in River Skjern (2020 and 2022), Denmark, and 82 smolts in River Ballycastle (2022), Northern Ireland, and their seaward movements were monitored using acoustic receivers deployed in the river, fjord, and coastal area. In River Skjern in 2022, comparator smolts migrated earlier than trapped smolts, likely because these constituted more of the larger‐sized, earlier migrating individuals. We found no differences in descent trajectories, diel patterns, progression rates, or survival between trapped smolts and comparator smolts in any of the rivers or study years. Thus, our results support the use of during‐migration trapping as a low‐impact method to capture smolts for telemetry studies, with trapped samples (if held <24 h) yielding comparable results in terms of behaviour and survival with non‐delayed pre‐migration tagged fish.
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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 | 0.001 | 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".