Coupling bio‐logging with nutritional geometry to reveal novel insights into the foraging behaviour of a plunge‐diving marine predator
Bibliographic record
Abstract
ABSTRACT It is widely believed that predators maximise their energy intake while foraging and consume prey that are nutritionally similar. We combined GPS data loggers, miniaturised cameras, dietary sampling and nutritional geometry to examine the nutritional variability in the prey and selected diet, and foraging performance, of the masked booby ( Sula dactylatra tasmani ), a wild carnivore and marine top predator. Data loggers also revealed no significant differences between sexes in the foraging performance of chick‐rearing adults. Females provided more food to their chicks than the males and, regardless of the nutritional variability of prey consumed, both sexes showed similar amounts of protein and lipid in their diets. Miniaturised cameras combined with nutritional analysis of prey provided, for the first time, fine‐scale detail of the amounts of macronutrients consumed in each plunge dive and the overall foraging trip. Our methodology could be considered for future studies that aim to contribute to the general understanding of the behavioural and physiological mechanisms and ecological and evolutionary significance of animal foraging (e.g. energy expenditure budgets and prey selection for self‐ and offspring‐feeding that could lead to sex‐specific foraging strategies).
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".