Seismic signals in a courting male jumping spider (Araneae:Salticidae)
Bibliographic record
Abstract
Visual displays in jumping spiders have long been known to be among the most elaborate animal communication behaviours. We now show that one species, Habronattus dossenus, also exhibits an unprecedented complexity of signalling behaviour in the vibratory (seismic) modality. We videotaped courtship behaviour and used laser vibrometry to record seismic signals and observed that each prominent visual signal is accompanied by a subsequent seismic component. Three broad categories of seismic signals were observed ('thumps', 'scrapes' and 'buzzes'). To further characterize these signals we used synchronous high-speed video and laser vibrometry and observed that only one seismic signal component was produced concurrently with visual signals. We examined the mechanisms by which seismic signals are produced through a series of signal ablation experiments. Preventing abdominal movements effectively 'silenced' seismic signals but did not affect any visual component of courtship behaviour. Preventing direct abdominal contact with the cephalothorax, while still allowing abdominal movement, only silenced thump and scrape signals but not buzz signals. Therefore, although there is a precise temporal coordination of visual and seismic signals, this is not due to a common production mechanism. Seismic signals are produced independently of visual signals, and at least three independent mechanisms are used to produce individual seismic signal components.
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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".