Group size, but not manipulated whole-clutch egg color, contributes to ovicide in joint-nesting Smooth-billed Anis
Bibliographic record
Abstract
Reproductive competition in the form of ovicide is common in some joint-nesting birds, species in which multiple females lay eggs in a single nest. Joint-nesting Smooth-billed Anis (Crotophaga ani) often bury eggs under a new nest floor prior to laying their own eggs. Anis apparently cannot recognize their own eggs, which raises the question of how individuals can minimize their loss of young due to egg burial or consequences of hatching asynchrony. Newly laid eggs are coated with a white layer of vaterite (calcium) while older eggs may be almost entirely blue. Leaving older, bluer eggs in the nest presumably leads to a more competitive nest environment for the female's own young because newer eggs hatch later, producing smaller chicks at risk of getting trampled or bumped out of the nest. We hypothesized that Smooth-billed Anis use egg color as a cue for egg age. We predicted females not yet laying would bury older-looking blue eggs and tested this by removing the vaterite layer from all eggs in selected nests. Contrary to our prediction, altering egg color of whole clutches to make them look bluer, and thus older, did not trigger egg burial. The number of eggs buried increased with increasing female group size, however, consistent with previous work on this species.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".