Individual assignment of Atlantic bluefin tuna in the northwestern Atlantic Ocean using single nucleotide polymorphisms reveals an increasing proportion of migrants from the eastern Atlantic Ocean
Bibliographic record
Abstract
Identifying the origin of fish contained in a mixed fishery is critical for accurate stock assessments and the subsequent development of appropriate management strategies. Using a panel of 92 single-nucleotide polymorphisms (SNPs) developed to differentiate Atlantic bluefin tuna (Thunnus thynnus) from the two main spawning areas (Gulf of Mexico and Mediterranean Sea), we used individual assignment to determine composition of feeding aggregations in the northwestern Atlantic Ocean (Gulf of Maine, Bay of Fundy, Scotian Shelf, Gulf of St. Lawrence, coastal Newfoundland). Among the 3163 individuals collected between 2004 and 2018, we found that among lower age groups (<15 years) the spawning stock providing the most recruits to the Canadian fishery transitioned from western Atlantic to Mediterranean origin over time. While the majority of older adults (>15 years) have consistently been of western Atlantic origin, the disparity in the proportional contribution of western and eastern spawning groups narrowed significantly over the 14-year study period. Our results can be used to inform population-specific exploitation rates, improve stock assessments, and identify age-dependent habitat use and areas suitable for additional conservation efforts.
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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 | 0.000 |
| 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".