COMPARING REPERTOIRES OF SPERM WHALE CODAS: A MULTIPLE METHODS APPROACH
Bibliographic record
Abstract
A common task for researchers of animal vocalisations is statistically comparing repertoires, or sets of vocalisations. We evaluated five methods of comparing repertoires of ‘codas’, short repeated patterns of clicks, recorded from sperm whale (Physeter macrocephalus) groups. Three of the methods involved classification of codas—human observer classification, k-means cluster analysis using Calinski and Harabasz's (1974) criterion to determine k, and a divisive k-means clustering procedure using Duda and Hart's (1973) criterion to determine k. Two other methods used multivariate distances to calculate similarity measures between coda repertoires. When used on a sample coda dataset, observer classification failed to produce consistent results. Calinski and Harabasz's criterion did not provide a clear signal for determining the number of coda classes (k). Divisive clustering using Duda and Hart's criterion performed satisfactorily and, encouragingly, gave similar results to the multivariate similarity measures when used on our data. However, the relative performance of the k-means techniques is likely data dependent, so one method is not likely to perform best in all circumstances. Thus results should be checked to ensure they extract logical clusters. Using these techniques concurrently with multivariate measures allows the drawing of relatively robust conclusions about repertoire similarity while minimising uncertainties due to questionable validity of classifications.
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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 | 0.032 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".