Seismic signal production in a wolf spider: parallel<i>versus</i>serial multi-component signals
Bibliographic record
Abstract
Animal signals can consist of multiple parts within or across sensory modalities (multi-component signals or multimodal signals). While recent work has focused on multimodal signals, the production, processing and evolution of multi-component signals has received considerably less attention. Here, using synchronous high-speed video and laser vibrometer recordings followed by experimental manipulations of putative sound-producing structures, we explored the mechanisms of seismic signal production in the courtship display of Schizocosa stridulans Stratton. Two types of seismic courtship signals were observed: 'rev' and 'idle' signals. Revs consist of a high-frequency component produced by flexions of the male pedipalp (stridulation) simultaneous with a low-frequency component produced by movements of the abdomen (tremulation). This multi-component signal is produced by independent structures and represents a parallel multi-component display. By contrast, idle displays consist of a high-intensity component produced by drumming of the forelegs on the substrate (percussion) followed by a high-frequency component produced by flexions of the male pedipalp (stridulation). While the components of the idle display are also produced by independent structures, the leg drumming and palp flexions occur serially and do not overlap in time. We discuss the selective pressures that may drive the evolution of multiple sound-producing structures as well as the selective pressures that drive the evolution of parallel versus serial multi-component signals.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".