Comparison of machine learning techniques for the classification of echolocation clicks from three species of odontocetes
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
A species detectorclassifier is presented which decides whether or not short groups of clicks are produced by one or more individuals from the following species: Blainvilleâs beaked whales, short-finned pilot whales, and Rissoâs dolphins. The system locates individual clicks using the Teager energy operator and then constructs feature vectors for these clicks using cepstral analysis. Two different types of detectors confirm or reject the presence of each species. Gaussian mixture models (GMMs) are used to model time series independent characteristics of the species feature vector distributions. Support vector machines (SVMs) are used to model the boundaries between each speciesâ feature distribution and that of other species. Detection error tradeoff curves for all three species are shown with the following equal error rates: Blainvilleâs beaked whales (GMM 3.32%/SVM 5.54%), pilot whales (GMM 16.18%/SVM 15.00%), and Rissoâs dolphins (GMM 0.03%/SVM 0.70%).
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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