Nonrandom patterns of roost emergence in big brown bats,<i>Eptesicus fuscus</i>
Bibliographic record
Abstract
In most colonial species of bats individuals emerge en masse from day roosts each evening to begin foraging. Although some aspects of emergence behavior are understood, one previously unexplored area is the specific order in which individuals emerge. The goal of our research was to determine if big brown bats, Eptesicus fuscus, fitted with passive integrated transponder tags emerge from roosts in buildings each evening in a nonrandom order. We assessed relative and absolute order of emergence to determine if order is concordant across nights and whether individuals consistently emerge in close association with specific roost mates. We found significant concordance in rank order among nights at all roosts. At 5 roosts concordance decreased as time between dates increased. Association rates between individuals were low, and temporal analyses revealed that associations rapidly degraded over time, indicating that bats do not emerge each evening consistently with the same group of roost mates. We discuss how social structure, information transfer, and/or individual energetic needs could be responsible for the observed nonrandom patterns of emergence. Our results suggest that emergence order represents behavioral information that traditionally has been overlooked and that might be useful for characterizing aspects of the ecology and social behavior of bats and other species with cryptic behavior.
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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 | 0.001 | 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".