Negotiation of parental care when the stakes are high: experimental handicapping of one partner during incubation leads to short‐term generosity
Bibliographic record
Abstract
1. Most game theoretical models of biparental care predict that a reduction in care by one partner should not be fully compensated by increased work of its mate but this may not be true for incubating birds because a reduction in care could cause the entire brood to fail. 2. I performed the first handicapping experiment of both males and females during incubation, by placing small lead weights on the tails of male and female northern flickers Colaptes auratus, a woodpecker in which males do most of the incubation. 3. Females responded to the acute stressor (handling and handicapping) by tending to abandon more readily than males and staying away from the nest longer in the first incubation bout. Among pairs that persisted, both males and females compensated fully for a handicapped partner, keeping the eggs covered nearly 100% of the time. 4. Partners did not retaliate by forcing their handicapped mate to sit on the eggs with a long incubation bout length subsequent to having a long bout length themselves. Instead, during the 24 h immediately after handicapping, males behaved generously by relieving handicapped females early. 5. Such generosity was probably not energetically sustainable as these male partners took on less incubation in the 72 h following handicapping compared to female partners of handicapped males. Males and females are probably generous in the short-term because of the high cost of nest failure during incubation but maintaining increased work loads in the longer term is probably limited by body condition and abandonment thresholds consistent with game theory models.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".