Identifying Areas of Potential Risk Based on Future Genetic Adaptability in Three Arctic Whale Species
Bibliographic record
Abstract
) are endemic Arctic whales adapted to cold-water conditions that depend on sea ice for foraging and protection from predators. We forecasted the degree of genetic mismatch these species may experience under future climate change scenarios by the next century using Canadian Arctic genomic samples in genotype-environment association models. When examining local adaptation to different environmental variables, we found that ice thickness was the strongest environmental predictor for bowhead whales, while temperature and chlorophyll concentration, an indicator of primary productivity, carried greater weight for beluga whales and narwhals. Notably, a higher degree of genetic mismatch for all three species was observed in the Canadian High Arctic and Hudson Bay, suggesting that whales in these areas may exhibit the greatest maladaptive risk to climate change. This multispecies assessment of Arctic-adapted whales provides insight into the spatial congruence between three genomic datasets and context for designing ecosystem-wide evolutionarily enlightened conservation strategies.
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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 | 0.001 |
| 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 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".