Inherent versus random variation in fitness of elephant seals: offspring quality and quantity
Bibliographic record
Abstract
Variation in reproductive success is the basis of evolution and allows species to respond to the environment, but only when it is based on fixed individual variation that is heritable. Several recent studies suggest that observed variation in reproduction is due to chance, not inherent individual differences. Our aim was to quantify inherent versus neutral variation in fitness of northern elephant seal ( Mirounga angustirostris (Gill, 1866)) females, including both quality and quantity of their offspring. Using 44 years of observations at Año Nuevo in California, we assembled lifetime pup production of 1065 individual females and mass at weaning for 2120 of their pups. Females varied significantly in mean lifetime mass of their pups, with 28% of the variance due to fixed individual differences among mothers. Variation was repeatable over 6 years of a mother’s lifetime and heritable ( h = 0.48). Moreover, pup mass at weaning was associated with future lifetime fitness, since larger pups had a higher chance of surviving to breed. Larger pups, however, did not produce more offspring once breeding, and lifetime pup production was not heritable. Traits related to offspring quality in elephant seals were inherently different among females, but variation in pup production was neutral.
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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 | 0.001 |
| 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".