Acoustic assessment of species richness and assembly rules in ensiferan communities from temperate ecosystems
Bibliographic record
Abstract
Vocalizing animals are known to produce a wide range of species-specific spectral and temporal communication patterns. As a consequence, the acoustic heterogeneity of insect communities is expected to increase with the number of vocalizing species. Using a combination of simulation models and field surveys, we tested the hypotheses that (1) acoustic heterogeneity increases with the number of cricket and katydid species in ensiferan communities and (2) acoustic heterogeneity of naturally assembled ensiferan communities is higher than that of randomly assembled ones. The slope of the acoustic heterogeneity--species richness relationship in naturally assembled communities was positive but did not differ from that of randomly assembled communities, suggesting a rather weak competition for the acoustic space. Comparing the species richness-acoustic heterogeneity relationship of naturally and randomly assembled communities, our study provides a novel approach for understanding species assembly rules in animal groups that rely on acoustic communication.
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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".