Differences in the foraging behaviour of male and female Egyptian fruit bats (<i>Rousettus aegyptiacus</i>)
Bibliographic record
Abstract
Male and female animals frequently have different foraging behaviours owing to differences in body size or nutritional demand, or to intraspecific competition. We studied foraging by Egyptian fruit bats ( Rousettus aegyptiacus (E. Geoffroy, 1810)) in Cape Town, South Africa, to test predictions based on differences in nutritional demand during reproduction. Using radiotelemetry, we compared emergence, return, and foraging times of males and females during pregnancy and lactation. We also determined home-range size, habitat use, and use of figs (genus Ficus L.), which are a potential source of calcium for lactating females. During the pregnancy period, males left their roost later than females and were away from the roost for shorter periods. There were no differences in timing of foraging during lactation. Females foraged in native forest more than males did, but home-range size did not differ. There was no evidence that females fed on figs more than males did. Differences in foraging behaviour were not as predicted based on nutritional and energetic differences. The small population may have meant that there was little competition for food, and figs may have provided a profitable source of energy for both males and females. Differences in the timing of foraging are best explained by the need for males to defend roosting sites.
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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) | 0.002 | 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".