Using identity calls to detect structure in acoustic datasets
Bibliographic record
Abstract
Abstract Acoustic analyses can be powerful tools for illuminating structure within and between populations, especially for cryptic or difficult to access taxa. Acoustic repertoires are often compared using aggregate similarity measures across all calls of a particular type, but specific group identity calls may more clearly delineate structure in some taxa. We present a new method—the identity call method—that estimates the number of acoustically distinct subdivisions in a set of repertoires and identifies call types that characterize those subdivisions. The method uses contaminated mixture models to identify call types, assigning each call a probability of belonging to each type. Repertoires are hierarchically clustered based on similarities in call type usage, producing a dendrogram with ‘identity clades’ of repertoires and the ‘identity calls’ that best characterize each clade. We validated this approach using acoustic data from sperm whales, grey‐breasted wood‐wrens and Australian field crickets, and ran a suite of tests to assess parameter sensitivity. For all taxa, the method detected diagnostic signals (identity calls) and structure (identity clades; sperm whale subpopulations, wren subspecies and cricket species) that were consistent with past research. Some datasets were more sensitive to parameter variation than others, which may reflect real uncertainty or biological variability in the taxa examined. We recommend that users perform comparative analyses of different parameter combinations to determine which portions of the dendrogram warrant careful versus confident interpretation. The presence of group‐characteristic identity calls does not necessarily mean animals perceive them as such. Fine‐scale experiments like playbacks are a key next step to understand call perception and function. This method can help inform such studies by identifying calls that may be salient to animals and are good candidates for investigation or playback stimuli. For cryptic or difficult to access taxa with group‐specific calls, the identity call method can aid managers in quantifying behavioural diversity and/or identifying putative structure within and between populations, given that acoustic data can be inexpensive and minimally invasive to collect.
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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 | 0.012 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".