Detection and classification of North Atlantic right whales in the bay of fundy using independent component analysis
Bibliographic record
Abstract
A novel method of detection and classification for marine mammals is presented which uses techniques from independent component analysis to solve the blind source separation problem for North Atlantic right whales (Eubalaena glacialis). Using the fundamentally non-Gaussian nature of marine mammal vocalizations and data collected on multiple hydrophones, we are able to separate right whale source spectra, up to an unknown scale, from ambient noise. This technique assumes that the array data is a linear combination of non-Gaussian source signals but does not require specific knowledge of the array geometry. A detection algorithm which separates right whale vocalizations from ambient background using a Kolmogorov-Smirnov test statistic is presented and tested on data collected in the Bay of Fundy. The performance of the detector was found to be such that it was possible to achieve a probability of detection of about three-fourths with a false alarm probability of about one-third. Independent component analysis was found to provide little improvement over standard principle component analysis, which was used as preprocessing step.
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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 | 0.000 |
| 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 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".