Discriminating Canadian Arctic beluga management stocks using dentine oxygen and carbon isotopes
Bibliographic record
Abstract
In the eastern Canadian Arctic, belugas Delphinapterus leucas exhibit genetic and ecological differentiation across their distribution that forms the basis of management stocks for traditional Inuit hunts. Using oxygen and stable carbon isotope ratios in dentine phosphate (δ18OP) and structural carbonate (δ13CSC), respectively, we evaluated the spatial structure of 3 of these beluga stocks: Western Hudson Bay (WHB, n = 30), Cumberland Sound (CS, n = 44), and Eastern High Arctic-Baffin Bay (EHA-BB, n = 29). Pairwise comparisons revealed significant differences in δ18OP and Suess-adjusted δ13CSC among all stocks, with the exception of similar δ18OP between the WHB and CS stocks. A linear discriminant analysis (LDA) model fit to 60% of the data set (training data) successfully classified 84% of the remaining belugas (test data) to their respective stocks based on hunt location. Isotopic overlap among stocks could reflect (1) homogeneous baseline stable isotope (SI) composition between geographically adjacent stocks, (2) some degree of marginal geographic overlap in ranges or individual movements among stocks, perhaps during late spring, or (3) confounding dietary influences that increased within-stock SI variation. Some misclassifications consistent with individual movements among stocks were supported by limited genetic data, with a small number of belugas hunted from the CS stock exhibiting both isotopic and genetic similarity to WHB belugas. Geographic stock differentiation inferred from oxygen and carbon isotope proxies largely corroborates current eastern Canadian Arctic beluga stock definitions, which is relevant not only for management purposes but also for monitoring changing beluga distributions in response to ongoing climate-driven changes in Arctic marine ecosystems.
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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 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 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".