Population structure of ice‐breeding seals
Bibliographic record
Abstract
The development of population genetic structure in ice-breeding seal species is likely to be shaped by a combination of breeding habitat and life-history characteristics. Species that return to breed on predictable fast-ice locations are more likely to exhibit natal fidelity than pack-ice-breeding species, which in turn facilitates the development of genetic differentiation between subpopulations. Other aspects of life history such as geographically distinct vocalizations, female gregariousness, and the potential for polygynous breeding may also facilitate population structure. Based on these factors, we predicted that fast-ice-breeding seal species (the Weddell and ringed seal) would show elevated genetic differentiation compared to pack-ice-breeding species (the leopard, Ross, crabeater and bearded seals). We tested this prediction using microsatellite analysis to examine population structure of these six ice-breeding species. Our results did not support this prediction. While none of the Antarctic pack-ice species showed statistically significant population structure, the bearded seal of the Arctic pack ice showed strong differentiation between subpopulations. Again in contrast, the fast-ice-breeding Weddell seal of the Antarctic showed clear evidence for genetic differentiation while the ringed seal, breeding in similar habitat in the Arctic, did not. These results suggest that the development of population structure in ice-breeding phocid seals is a more complex outcome of the interplay of phylogenetic and ecological factors than can be predicted on the basis of breeding substrate and life-history characteristics.
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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 | 0.000 |
| 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) | 0.002 | 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".