Quantifying the Effects of Propagation on Classification of Cetacean Vocalizations
Bibliographic record
Abstract
To develop a robust automatic classifier with a high probability of detection and a low false alarm rate that can classify vocalizations from a variety of cetacean species in diverse ocean environments. OBJECTIVESIn previous work as part of ONR grant N000141210139 a unique automatic classifier developed by the PI that uses perceptual signal features -features similar to those employed by the human auditory system-was employed to successfully classify anthropogenic transients, and vocalizations from five cetacean species.Although this is a significant achievement, successful implementation of this (or any) classifier requires that it be temporally and spatially robust.The primary goal will be to address the question: "Will it work on vocalization data from these species collected under different environmental conditions?"To examine this, discriminant analysis will be used to rank the aural features in terms of their ability to separate the vocalizations between species.Then, the more highly ranked features will be tested for robustness.This will be done by performing a propagation experiment using cetacean vocalizations and synthetically generated calls as source signals, and testing the received signals with the classifier.The measurements will be complemented by comparing experimental results to propagation model results with the goal of generalizing the results to other ocean environments. APPROACH
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".