Spatiotemporal dynamics of the Atlantic salmon (Salmo salar) Greenland fishery inferred from mixed-stock analysis
Bibliographic record
Abstract
Mixed-stock fisheries refer to the exploitation of admixed fish stocks coming from different origins. We identified the North American origin of 2835 Atlantic salmon ( Salmo salar ) in the Greenland mixed-stock fishery during 11 years (1995–2006) at three localities using 13 microsatellites. The study included 52 baseline populations representing nine genetically distinct regional groups. The contribution of each group ranged from <1% (Maine) to 40% (Southern Québec). Decreasing temporal contributions were observed for Southern Québec (–22.0%) and New Brunswick (–17.4%), whereas an increasing contribution for Labrador (+14.9%) was observed during the time course of the study. The estimated regional contribution to the Greenland fishery was significantly correlated to the number of multi-sea-winter salmon regionally produced in 2002 (r = 0.79) and 2004 (r = 0.92). No difference in contribution was found between the three Greenland sampling localities. Ungava and Southern Québec regions showed the highest mortality estimates caused by the fishery, ranging from 12.10% to 18.08%, for both years tested. No regional group was overrepresented in landings compared with their respective productivity. Yet, management precautions should still be taken as the fishery strongly selects large females, which could have evolutionary impacts on populations over the long term.
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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 | 0.001 | 0.001 |
| 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".