Molecular Genetic Stock Discrimination of Belugas (<i>Delphinapterus leucas</i>) Hunted in Eastern Hudson Bay, Northern Quebec, Hudson Strait, and Sanikiluaq (Belcher Islands), Canada, and Comparisons to Adjacent Populations
Bibliographic record
Abstract
Belugas (Delphinapterus leucas) harvested from communities on the eastern Hudson Bay (EHB) arc, Sanikiluaq on the Belcher Islands, northwestern Quebec, Hudson Strait, neighboring areas of Hudson Bay, and the St. Lawrence were characterized by differences in the mitochondrial DNA (mtDNA) d-loop sequence and in 15 nuclear microsatellite loci. Results supported the hypothesis that communities outside the EHB arc hunt some EHB belugas, which were strongly differentiated from all neighboring sample populations by mtDNA haplotypes and weakly differentiated by microsatellite data. Belugas genetically most similar to those sampled in EHB comprised 19% of the harvest in Hudson Strait and Ungava, 15% in northwestern Quebec, 9% in western and northern Hudson Bay, 8% in Sanikiluaq, and 5% in Kimmirut (though many were possibly not belugas from EHB, but uncommon genotypes in other stocks). Within EHB, belugas from the Nastapoka River (1984-95) and elsewhere on the EHB arc (1993-97) were very similar. Using simple probabilistic calculations to assign individuals to their most likely sample population, we estimated that 15% of belugas hunted in EHB could be from northern or western Hudson Bay and 3% from Sanikiluaq. St. Lawrence River belugas were strongly differentiated from all other sample populations by both haplotypes and microsatellites. Stocks in Arctic populations were identified by different proportions of alleles and by genetic consistency over several years. Belugas from Sanikiluaq, Kimmirut, and EHB may represent three separate stocks, while large genetic diversities in northern Quebec, northern Hudson Bay, and Arviat confirm that mixtures of stocks were harvested in these areas.
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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 | 0.001 | 0.001 |
| 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".