Pupping-site fidelity among individual Steller sea lions (Eumetopias jubatus) at Chiswell Island, Alaska
Bibliographic record
Abstract
Pupping-site fidelity (defined as pupping within 5.8 m of a previous location in ≥2 years) in Steller sea lions ( Eumetopias jubatus (Schreber, 1776)) was investigated during the breeding seasons from 2001 to 2005 at Chiswell Island, a small rookery in the Gulf of Alaska. Density of females was minimal with 54–80 pups born. Photo-identification of individuals and GPS points, including elevation of the rookery, were used to determine locations of 297 births. Sixty-four percentage (35/55) of the females exhibited pupping-site fidelity, and mean frequency of site fidelity was greater for females that gave birth >3 times. Fifty percentage of the births occurred at 22% of the locations for all years, indicating that multiple births occurred at some locations throughout the breeding season. Competition or aggression among females for specific pupping locations before birth was not observed, and there was no temporal order of pupping locations used, indicating that the rookery was not overcrowded and that many suitable pupping locations existed. Multiparous females gave birth at significantly greater distances from the surf zone than primiparous females. Long-term monitoring of pupping-site fidelity in combination with other measures of maternal care will ultimately determine which factors have the greatest effect on pup survival.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".