Parental Effort and Reproductive Skew in Coalitions of Brood Rearing Female Common Eiders
Bibliographic record
Abstract
Members of breeding groups face conflicts over parental effort when balancing antipredatory vigilance and feeding. Empirical evidence has shown disparate responses to manipulations of parental effort. We develop a model in which we determine the evolutionarily stable effort of partners given their body conditions, allowing the benefits of shared care to be unevenly divided, and we test this model's predictions with data on common eiders (Somateria mollissima). Eiders show uniparental female care; females may share brood rearing, or they may tend alone, and their body condition at hatching of the young shows large environmentally induced variation. The model predicts that parental effort (vigilance) in a coalition is lower than when tending alone, controlling for parental condition; this prediction is supported by the data. The parental effort in a coalition should be positively correlated with body condition, and this prediction is also supported. Finally, parental effort should increase when partner condition decreases and vice versa; this prediction is partially supported. The Nash bargaining game may provide promising avenues by which to determine the precise settlement of reproductive skew and effort between coalition partners in the future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| 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 | 0.001 |
| 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".