Age at weaning of California sea lions depends on colony latitude
Bibliographic record
Abstract
The age at which sea lions wean can vary significantly between years and among populations. It is an important life-history parameter that is influenced by environmental conditions and can drive changes in sea lion numbers. However, knowing when weaning begins and ends is difficult to determine. We developed a method (using Fourier analysis) to identify the lactation period from changes in the δ 15 N profiles of vibrissae from juvenile California sea lions—born in three colonies in Mexico. We sectioned vibrissae from 15 juvenile California sea lions (aged approximately 12 months) into 33–74 segments of similar weight. We measured δ 15 N and δ 13 C for each vibrissa segment and assigned dates to each one using site-specific vibrissa growth rates. We also compared the δ 15 N profiles that corresponded to the pup stage on the juvenile vibrissae with δ 15 N values of adult female vibrissae from the same colonies to validate the dietary transition from milk to fish identified by the Fourier analysis. We found that pups began supplementing their milk diet with fish at different times between colonies—ranging from 3 to 5 months old (San Esteban Island), 5–7 months old (Santa Margarita Island), and > 12 months old (Los Islotes Island). All pups were > 1 year old when weaned. The longer lactation period in Mexico contrasts with the shorter 10–11 months age at weaning recorded at northern colonies of California sea lions along the US Pacific coast (San Miguel Island). The difference in lactation duration among regions likely reflects latitudinal differences in marine productivity, and a lower nutrition quality of prey available to California sea lions in Mexico. Our study augments the limited knowledge of weaning in California sea lions and provides a means to determine weaning in other species.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".