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 Rgen (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 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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".