Regional differences in the whistles of Australasian humpback dolphins (genus <i>Sousa</i>)
Bibliographic record
Abstract
Characteristics of whistles may be used to study differentiation in dolphins to complement morphological and genetic studies. The whistles of four populations of Chinese humpback dolphins (Sousa chinensis chinensis (Osbeck, 1765)), one population of Taiwanese humpback dolphins (Sousa chinensis taiwanensis Wang, Yang, and Hung, 2015), and one population of Australian humpback dolphins (Sousa sahulensis Jefferson and Rosenbaum, 2014) were compared to determine if differences in whistles support current views of population structure and regional and species differentiation in the genus Sousa Gray, 1866. Acoustic features were extracted from whistles captured by broadband recording systems. Permutational MANOVAs were conducted to test for differences between populations, regions, and species. Random forest trees were also used to classify similar whistles. A significant amount of variation in acoustic features was explained by population (pseudo F[5,2742] = 191.66, p < 0.001), regional (pseudo F[3,2741] = 280.62, p < 0.001), and species (pseudo F[1,999] = 3.7, p < 0.05) differences in humpback dolphin whistles. Random forest trees correctly classified whistles into populations from 40% to 67%, regions from 51% to 80%, and species from 74% to 80%. Differences in whistles were consistent with the current ideas of population-, regional-, and species-level differences within the genus Sousa, based on morphological and genetic data, as well as geographic distance and barriers to movement.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".