Acoustic communication in the Palaearctic red cicada,<i>Tibicina haematodes</i>: chorus organisation, calling-song structure, and signal recognition
Bibliographic record
Abstract
Males of the Palaearctic red cicada, Tibicina haematodes, produce calling songs that are attractive to both sexes. For the first time we (i) describe the organisation of the chorus formed by aggregating males, (ii) analysed the physical characteristics of the calling song, and (iii) used playback experiments of natural, modified, and allospecific signals to investigate the signal-recognition process. Males overlap each other's calling song and try to call first and last during a chorus, leading to what we term domino and last-word effects, respectively. The calling song consists of a two-part sequence made up of a succession of pulses. It is characterized by slow and fast amplitude modulations and three frequency bands. The structure of the signal varied among individuals in both temporal and frequency parameters. Our playback experiments showed that males make a rough analysis of frequency and duration features of the signal. They pay no attention to amplitude modulations. Because males are not capable of precise analysis, they reply to various allospecific calling songs. Females' analysis of the calling song being difficult to test, the role of this signal in sexual selection still needs to be documented.
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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".