Crowd intelligence can discern between repertoires of killer whale ecotypes
Bibliographic record
Abstract
Call classifications by human observers are often subjective yet they are critical to studies of animal communication, because only the categories that are relevant for the animals themselves actually make sense in terms of correlation to the context. In this paper we test whether independent observers can correctly detect differences and similarities in killer whale repertoires. We used repertoires with different a priori levels of similarity: from different ecotypes, from different oceans, from different populations within the same ocean, and from different local subpopulations of the same population. Calls from nine killer whale populations/subpopulations were pooled into a joint sample set, and eight independent observers were asked to classify the calls into separate categories. None of the observers’ classifications strongly followed the known phylogeny of the analyzed repertoires. However, some phylogenetic relationships were reflected in the classifications substantially better than others. Most observers correctly separated the calls from two North Pacific ecotypes. Call classifications averaged across multiple observers reflected the known repertoire phylogenies better than individual classifications, and revealed the similarity of repertoires at the level of subpopulations within the same population, or closely related populations.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".