Exploiting vulnerable prey: moths and red bats (<i>Lasiurus borealis</i>; Vespertilionidae)
Bibliographic record
Abstract
We observed 18 individually banded red bats, Lasiurus borealis, foraging around streetlights to test our hypotheses that they were either foraging cooperatively or practising kleptoparasitism (theft of prey). In 80 of 238 attacks, bats reattacked the same moth (29% of these attacks involved >1 bat and 71% just 1 bat). Logistic regression showed that a bat's foraging-success rate was significantly positively affected by the number of attacks made on a moth (p < 0.05) and the type of attack (by a single bat versus >1 bat) (p < 0.05) but negatively affected by the length of time over which the moth was attacked (i.e., from the first to the second attack) (p < 0.05). Using a model we tested whether or not an eavesdropping L. borealis could be in a position to reattack a vulnerable (previously attacked) moth before the initial attacker and found that if an eavesdropper was within 30 m during the first attack it could always beat the first attacking bat to the vulnerable moth. The data and analysis support neither the cooperative-foraging nor the kleptoparasitism hypotheses, but rather show that a combination of timing of moth defensive behaviour and bat flight performance strongly influences the outcome of an attack.
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 | 0.000 |
| 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".