Regulation of chick provisioning in the thin-billed prion: an interannual comparison and manipulation of parents
Bibliographic record
Abstract
To investigate how thin-billed prions, Pachyptila belcheri, regulate their chick-provisioning behaviour, we compared data from two seasons with different food availability (1992 and 1997) and increased the costs of foraging by using load bands during 1997. The 1992 breeding season was poorer than 1997, but during both seasons parents used a two-part foraging strategy, mixing long trips, when they built up body reserves, with short trips, when they used previously stored reserves and foraged to feed the chick. In 1992, parents alternated long and short trips and spent more time on long trips, whereas in 1997 they performed several short trips before a long trip. As a consequence, provisioning rates were higher in 1997. Similarly, manipulated birds decreased their provisioning rates compared with control birds. During the two seasons, both control and experimental parents kept their body mass constant, and survival rates did not differ between seasons or groups. Using a logistic regression, we examined the factors influencing the decision whether, after a first short trip, to start a long trip or a second short trip. This decision was not influenced by the body condition of the chick, the meal mass, or the duration of the previous foraging trip, only by adult body condition. The results show that parents regulate their provisioning according to their body mass, which is the main factor influencing decisions concerning energy allocation between current reproduction and survival in this small long-lived seabird.
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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 | 0.000 |
| 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".