The genetic composition of feeding aggregations of the Atlantic mackerel (Scomber scombrus) in the central north Atlantic: a microsatellite loci approach
Bibliographic record
Abstract
Abstract The impacts of climate change on marine ecosystems can be seen in the changing distribution, migration, and abundance of species in the oceans. For some species this changing environment may be beneficial and can support population expansions. In the northeast Atlantic (NEA), the Atlantic mackerel (Scomber scombrus) is undergoing an increase in stock size accompanied by changing summer migration patterns, which have resulted in an expansion further north and north west than previously recorded. This study uses microsatellite loci to confirm the differentiation among NEA and northwest Atlantic (NWA) mackerel spawning populations and to assess the level of structuring within these populations. In addition, to enable population-specific exploitation rates to be factored into fisheries management, we identified the origin of individuals composing the expanding feeding aggregations in the central north Atlantic (Greenland, Iceland, Faroes), with all aggregations tested originating from spawning populations in the NEA. This study showed that microsatellite loci were useful to assess the contribution of NEA and NWA populations to mixed feeding aggregations across the north Atlantic for large pelagic fish stocks but were not powerful enough to evaluate the specific contribution of known stocks within NEA and NWA.
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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".