Crowd intelligence can discern between repertoires of killer whale ecotypes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it