Bibliographic record
Abstract
Relatively little is known about how sensory information is used for controlling flight in birds. A powerful method is to immerse an animal in a dynamic virtual reality environment to examine behavioral responses. The research comprising this dissertation investigated the role of vision during free flight hovering in hummingbirds to determine how optic flow –image movement across the retina– is used to control body position. We filmed hummingbirds hovering in front of a projection screen with the prediction that stationary patterns would allow a hummingbird to maintain stable body position, but moving patterns would change hovering stability. When hovering in the presence of moving gratings and spirals, hummingbirds lost positional stability and responded in the direction of the stimulus motion. There was no loss of stability with stationary patterns (Chapter 1). How sensitive are hummingbirds to visual motion? We predicted that small changes in the direction of a looming motion would result in matched changes in backward flight response of hummingbirds. Providing stationary visual patterns in combination with looming spirals was predicted to rescue hovering stability. Our results suggest that hummingbirds are not only sensitive to small changes in motion direction, but also sensitive to any visual motion of the background, even when large stationary features are present (Chapter 2). The sensitivity of hovering hummingbirds to visual motion suggested that other senses might be involved to stabilize flight. When docked with a feeder, hummingbirds gain a stable physical reference through bill contact. We predicted that tactile feedback during docked feeding would provide the necessary stationary reference to help hummingbirds override their sensitivity to visual motion. We built an instrumented feeder that measured how much a docked hummingbird pushed laterally and vertically. Hummingbirds were not very precise during docked hovering and pushed against the feeder in an attempt to stabilize left, right, and downward visual motions. Upward motion was not matched by pushing against the feeder (Chapter 3). Collectively, these experiments demonstrate that hummingbirds control hovering position by stabilizing motions in their visual field both when hovering in space and when docked with their bill inserted into a flower.
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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 | 0.000 |
| 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) | 0.001 | 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".