Toward comprehensive Chiroptera modeling: A parametric multiagent model for bat behavior
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Chiroptera behavior is complex and often unseen as bats are nocturnal, small, and elusive animals. Chiroptology has led to significant insights into the behavior and environmental interactions of bats. Biology, ecology, and even digital media often benefit from mathematical models of animals including humans. However, the history of Chiroptera modeling is often limited to specific behaviors, species, or biological functions and relies heavily on classical modeling methodologies that may not fully represent individuals or colonies well. This work proposes a continuous, parametric, multiagent, Chiroptera behavior model that captures the latest research in echolocation, hunting, and energetics of bats. This includes echolocation‐based perception (or lack thereof), hunting patterns, roosting behavior, and energy consumption rates. We proposed the integration of these mathematical models in a framework that affords the individual simulation of bats within large‐scale colonies. Practitioners can adjust the model to account for different perceptual affordances or patterns among species of bats, or even individuals (such as sickness or injury). We show that our model closely matches results from the literature, affords an animated graphical simulation, and has utility in simulation‐based studies.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it