Unpredictable hummingbirds: Flight path entropy is constrained by speed and wing loading
Bibliographic record
Abstract
ABSTRACT Unpredictable movement can provide an advantage when animals avoid predators and other threats. Previous studies have examined how varying environments can elicit unpredictable movement, but the intrinsic causes of complex, unpredictable behavior are not yet known. We addressed this question by analyzing >200 hours of flight performed by hummingbirds, a group of aerial specialists noted for their extreme agility and escape performance. We used information theory to calculate unpredictability based on the positional entropy of short flight sequences during 30-min and 2-hour trials. We show that a bird’s entropy is repeatable, with stable differences among individuals that are negatively correlated with wing loading: birds with lower wing loading are less predictable. Unpredictability is also positively correlated with a bird’s overall acceleration and rotational performance, and yet we find that moment-to-moment changes in acceleration and rotational velocities do not directly influence entropy. This indicates that biomechanical performance must share an underlying basis with a bird’s ability to combine maneuvers into unpredictable sequences. Contrary to expectations, hummingbirds achieve their highest entropy at relatively slow speeds, pointing to a fundamental trade-off whereby individuals must choose to be either fast or unpredictable.
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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".