Offspring mass variation in tree swallows: A case of bet‐hedging?
Bibliographic record
Abstract
Abstract The evolution of reproductive strategies is affected by the ability of organisms to deal with future environmental conditions. When environments are temporally unpredictable, however, it is difficult to anticipate optimal offspring phenotype. Diversification of offspring phenotypes, a strategy called diversified bet‐hedging, may allow parents to maximize their fitness by reducing between‐year variation in reproductive success. The link between diversification of offspring phenotypes and individual reproductive success, however, has rarely been documented empirically. We used an eight‐year dataset (1215 broods, 870 females) on individually marked tree swallows (Tachycineta bicolor) to assess whether intra‐brood mass variation was compatible with a diversified bet‐hedging strategy. Intra‐brood mass variation was weakly, but significantly repeatable within females, suggesting consistent individual differences. Greater intra‐brood mass variation, however, was not associated with reduced between‐year variation in reproductive success or increased female reproductive success. Moreover, contrary to diversified bet‐hedging expectations, fledging success of large broods was greater when hatchlings had similar rather than variable masses. Our results suggest that intra‐brood mass variation may not result from diversified bet‐hedging, but rather from complex interactions between environmental, brood, and maternal characteristics.
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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 | 0.002 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".