PINNIPED LACTATION STRATEGIES: EVALUATION OF DATA ON MATERNAL AND OFFSPRING LIFE HISTORY TRAITS
Bibliographic record
Abstract
Abstract Interspecific correlations are commonly used to explore the adaptive functions of life history traits in pinnipeds. Although adaptive conclusions are improved by the use of comparative methods that account for underlying phylogenetic relationships among species, they are still dependent on the quality of life history data. We collected pinniped species estimates for 12 maternal and offspring life history traits and evaluated these estimates based on sample size, duration of study, and methods used to obtain the data. Although excellent data exist for some species, high‐quality estimates in all 33 species are not available for any of the traits studied. High‐quality estimates of maternal postpartum mass are known for 12 species, neonate birth mass for 21, pup rate of mass gain for 12, lactation length for 10, and weaning mass for 10. High‐quality estimates of milk composition, milk energy output, and maternal foraging behavior during lactation are limited to ≤ 50% of species, with a taxonomic bias favoring the larger phocid species. Obtaining data on small‐bodied phocid species will be critical to gaining a better understanding of the relative roles of body size and phylogeny in the evolution of pinniped lactation strategies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".