Signals of need and quality: Atlantic puffin chicks can beg and boast
Bibliographic record
Abstract
Need and hunger models of honest begging predict that lower-quality offspring should call more, or beg, to signal their poor body condition or hunger. In contrast, quality models of begging predict that offspring of higher fitness should call more, or boast, to signal their viability to parents. We observed 2 types of calls in Atlantic puffin (Fratercula arctica) chicks: a shorter peep call and a longer screech call. Poorly fed chicks screeched during a higher proportion of parental visits than well-fed chicks. Food-supplemented chicks showed a decrease in the proportion of food visits with screech calls, whereas control chicks did not. Chicks in good body condition peeped more than chicks in poor body condition and these chicks showed a greater increase in the peep call rate after supplemental feeding than chicks that started off in poorer condition. Screech calls may signal need and/or hunger to parents, whereas peep calls may signal chick quality. This combination of signals should allow parents to make strategic resource-based decisions, allocating more food to hungry or lower-quality chicks when resources are abundant and preferentially feeding high-quality chicks when resources are scarce.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".