Recognition of variable courtship song in the field cricket<i>Gryllus assimilis</i>
Bibliographic record
Abstract
We analyzed the courtship song of the field cricket Gryllus assimilis. The song comprises two elements: groups of ca. 10 pulses (chirps) with low fundamental frequency (3.5-3.7 kHz) alternating with high-frequency (15-17 kHz) pulses (ticks) that usually occur as doublets. Some elements of courtship song are quite variable (high coefficient of variation) both within and between males, whereas others are more stereotypical. In experiments with playback of synthesized courtship songs, we studied the importance of several song parameters for mating success, which we evaluated as the probability with which females mounted muted, courting males. Altering some features that show little variability, such as chirp-pulse rate or carrier frequency of ticks, resulted in significant decreases in mounting frequency, consistent with the notion that trait values showing little variability are constrained by stabilizing selection exerted by females. However, alteration of one invariant trait, the occurrence of both song components, by omitting either component from test songs only slightly affected female responsiveness. Alteration of a variable song trait, the number of ticks per song phrase, had no effect on female response rate, thus failing to provide support for the idea that variable traits provide a substrate for sexual selection. An unusual characteristic feature of the song of G. assimilis is that chirp pulses often contain substantial high-frequency power, and indeed may entirely lack power at the fundamental frequency. Playback experiments showed that such songs are, nevertheless, behaviorally effective. To understand the neural basis for this, we recorded the responses of the two principal ascending auditory interneurons of crickets, AN1 and AN2. Our results suggest that the frequency selectivity of the neurons is sufficiently broad to tolerate the spectral variability of courtship chirps.
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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 | 0.001 | 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".