Genetic mixed-stock analysis of Atlantic herring populations in a mixed feeding area
Bibliographic record
Abstract
Determining spatio-temporal distributions of fish populations is of interest to marine ecology, in general, and to fisheries science in particular.Genetic mixed-stock analysis is routinely applied in several anadromous fishes for determining migratory routes and timing but has rarely been used for marine fishes, for which population differentiation is commonly weak and the method presumably less powerful.We used microsatellite information for Northeast Atlantic herring Clupea harengus L. populations and mixed stocks to address 2 questions.We used simulated mixture samples and 3 different statistical approaches to determine whether mixed stock composition could be determined with accuracy.Simulations showed that the applied approaches and mixture samples of 100 individuals enabled detailed composition analyses on a regional level, with resolution for tracing the ecologically dominant Rügen (Greifswalder Bodden) herring population.We then estimated spatio-temporal variation in herring migratory behaviour in the Skagerrak from 17 mixed samples collected over 2 seasons and 2 yr, and identified hitherto undescribed differences in distributions among populations that feed and winter in the area.
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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 | 0.001 | 0.002 |
| 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) | 0.000 | 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".