Fine‐scale study finds limited evidence of kin clustering in a gray seal colony
Bibliographic record
Abstract
Abstract Microsatellites were used to investigate fine‐scale spatial and temporal genetic structure of a gray seal breeding colony, using samples collected throughout the colony (1997) and more extensive local sampling (2000–2002). Previous behavior of breeding seals on North Rona, including philopatry and high breeding site fidelity, suggested female kin may cluster together. However, low FST scores indicated no genetic differentiation between the major breeding aggregations in different regions of the colony. Nevertheless, the pairwise relatedness of mothers within regions was significantly higher than the relatedness of mothers between regions. Evidence of kin clustering occurred within one region in the colony. Within this region, mothers' pairwise relatedness decreased significantly with increasing distance between pupping sites. Pairwise relatedness of neighboring females within this region was also higher than expected. Conversely, in the other regions mothers that were considered likely to have social interactions, based on their spatial and temporal proximity, were not more related to each other than random. Therefore, sustained philopatry appears sufficient to produce differences in relatedness at a within‐colony scale (>500 m), but not at finer scales. This suggests that long‐term associations of mothers on North Rona detected previously are unlikely to be formed solely by close kin.
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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 | 0.000 |
| 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) | 0.001 | 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".