Escaping from multiple visual threats: modulation of escape responses in Pacific staghorn sculpin ( <i>Leptocottus armatus</i> )
Bibliographic record
Abstract
Fish perform rapid escape responses to avoid sudden predatory attacks. During escape responses, fish bend their bodies into a C-shape and quickly turn away from the predator and accelerate. The escape trajectory is determined by the initial turn (stage 1) and a contralateral bend (stage 2). Previous studies have used a single threat or model predator as a stimulus. In nature, however, multiple predators may attack from different directions simultaneously or in close succession. It is unknown whether fish are able to change the course of their escape response when startled by multiple stimuli at various time intervals. Pacific staghorn sculpin (Leptocottus armatus) were startled with a left and right visual stimulus in close succession. By varying the timing of the second stimulus, we were able to determine when and how a second stimulus could affect the escape response direction. Four treatments were used: a single visual stimulus (control); or two stimuli coming from opposite sides separated by a 0 ms (simultaneous treatment), 33 ms or 83 ms time interval. The 33 ms and 83 ms time intervals were chosen to occur either side of a predicted 60 ms visual escape latency (i.e. during stage 1). The 0 ms and 33 ms treatments influenced both the escape trajectory and the stage 1 turning angle, compared with a single stimulation, whereas the 83 ms treatment had no effect on the escape trajectory. We conclude that Pacific staghorn sculpin can modulate their escape trajectory only between stimulation and the onset of the response, but the escape trajectory cannot be modulated after the body motion has started.
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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".