Trade-off between offspring mass and number: the lightest offspring bear the costs
Bibliographic record
Abstract
Abstract Life-history theory predicts a trade-off between offspring size and number. However, the role of intra-litter phenotypic variation in shaping this trade-off is often disregarded. We compared the strength of the relationship between litter size and mass from the perspective of the lightest and the heaviest yearling offspring in 110 brown bear litters in Sweden. We showed that the mass of the lightest yearlings decreased with increasing litter size, but that the mass of the heaviest yearling remained stable, regardless of litter size. Consistent with a conservative reproductive strategy, our results suggest that mothers maintained a stable investment in a fraction of the litter, while transferring the costs of larger litter size to the remaining offspring. Ignoring intra-litter phenotypic variation may obscure our ability to detect a trade-off between offspring size and number.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".