An effective method for the recapture of escaped farmed salmon
Why this work is in the frame
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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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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 it