Weaning age variation in beluga whales (<i>Delphinapterus leucas</i>)
Bibliographic record
Abstract
Beluga whales (Delphinapterus leucas) have a protracted nursing period estimated to last from 6–32 months, although current estimates of beluga nursing duration are derived using approaches subject to capture bias. Recent studies have shown stable isotope profiles of dentin growth layer groups (GLGs) in marine mammal teeth serve as a reliable nursing proxy and can be used to assess individual weaning patterns. We measured stable isotope ratios of nitrogen (δ15N) and carbon (δ13C) of dentin GLGs in teeth from eastern Canadian Arctic belugas to estimate weaning age and assess relative contributions of milk and solid food during the nursing period. δ15N declines of ~1‰ over the first 3 GLGs of most individuals were interpreted as evidence of weaning. Individual δ15N profiles indicated 15 of 27 whales were completely weaned by the end of their 2nd year, although a number of whales were weaned by the end of their 1st or 3rd year (9 and 3, respectively). Intermediate GLG2 δ15N values relative to GLGs 1 and 3 indicated most whales consumed a mixture of milk and solid food during their 2nd year, consistent with gradual weaning. Contrary to predictions based on parental care theory, nursing duration was not related to relative GLG width (used as a proxy for somatic growth) and did not differ for females and males, or among populations. δ13C variation was not a reliable indicator of nursing duration, as approximately half of the whales showed no ontogenetic δ13C patterns across GLGs deposited over the nursing period. This study provides novel life history information, which may inform beluga conservation and management decisions, and indicates belugas share prolonged nursing duration marked by individual variation observed in other odontocetes.
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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 | 0.000 |
| 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".