Acoustic feature recognition in the dogbane tiger moth,<i>Cycnia tenera</i>
Bibliographic record
Abstract
Certain tiger moths (Arctiidae) defend themselves against bats by phonoresponding to their echolocation calls with trains of ultrasonic clicks. The dogbane tiger moth, Cycnia tenera, preferentially phonoresponds to the calls produced by attacking versus searching bats, suggesting that it either recognizes some acoustic feature of this phase of the bat's echolocation calls or that it simply reacts to their increased power as the bat closes. Here, we used a habituation/generalization paradigm to demonstrate that C. tenera responds neither to the shift in echolocation call frequencies nor to the change in pulse duration that is exhibited during the bat's attack phase unless these changes are accompanied by either an increase in duty cycle or a decrease in pulse period. To separate these features, we measured the moth's phonoresponse thresholds to pulsed stimuli with variable versus constant duty cycles and demonstrate that C. tenera is most sensitive to echolocation call periods expressed by an attacking bat. We suggest that, under natural conditions, C. tenera identifies an attacking bat by recognizing the pulse period of its echolocation calls but that this feature recognition is influenced by acoustic power and can be overridden by unnaturally intense sounds.
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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".