Age and food hardness affect food handling by insectivorous bats
Bibliographic record
Abstract
Eating behaviour can vary with age, experience, and gender, as well as food hardness. This variation can contribute to intraspecific dietary differences and may result in variable definitions of optimal foraging and decreased intraspecific competition. We quantified feeding behaviour of insectivorous bats eating hard and soft mealworm-based food items based on the bats’ ability to consume and manipulate food items, consumption time, chew frequency, and total chews to consume. Adult Myotis lucifugus (LeConte, 1831) were more successful at both consuming and manipulating mealworms and consumed mealworms more quickly, with greater chew frequency and in fewer chews, than did subadults. Adults chewed mealworm viscera more frequently than did subadults but showed no differences in the other variables. Adult Eptesicus fuscus (Beauvois, 1796) consumed mealworms more quickly and with fewer chews than did subadults but showed no differences in the other variables. There were no differences between adult and subadult E. fuscus when consuming mealworm viscera. Male and female M. lucifugus did not differ significantly when eating either mealworms or mealworm viscera. There was no change in subadult consumption time of mealworms over the summer. Age-based differences in eating abilities may play a role in defining optimal foraging and dietary composition in insectivorous bats.
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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) | 0.002 | 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".