An effective method for the recapture of escaped farmed salmon
Bibliographic record
Abstract
The search for effective strategies to prevent and mitigate accidental releases of aquaculture fishes is on-going.To test a new recapture strategy and evaluate the individual dispersal behaviour of escaped farmed Atlantic salmon Salmo salar L. at the northern limit of its range, 39 adult salmon (mean ± SD fork length and weight: 85.5 ± 5.0 cm and 7.4 ± 1.4 kg, respectively) were implanted with depth-sensing acoustic tags and released in a north Norwegian fjord during the spring of 2007.The fish were released from 2 aquaculture sites in the Altafjord system and tracked using both mobile and fixed receivers.The coastal marine bag-net fishery, in combination with inriver angling, was tested as a potential recapture strategy.Immediately following the simulated escape event, the fish dove to near-bottom depths, subsequently returning to surface levels within the following days.The fish dispersed rapidly (9.5 ± 19.2 km d -1 ), traveling outward to coastal waters along the edges of the fjord.The bag-net fishers and anglers recaptured 79% of the escaped fish within 1 mo post-release, 90% of which were from bag nets.While most of the fish left the fjord, 7 tagged fish (18%) entered the Alta River estuary (3 of which later migrated up the Alta River), and 1 returned to the Altafjord the following year, presumably to spawn.The results showed that recapture efforts need to be immediate and widespread to mitigate farm-escape events.Coastal bag nets were effective at recapturing escaped farmed salmon, compared to previously tested methods, and would be especially useful in areas where gill-netting is not permitted.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".