Range-dependent impacts of ocean acoustic propagation on automated classification of transmitted bowhead and humpback whale vocalizations
Bibliographic record
Abstract
Significant effort has been made over the last few decades to develop automated passive acoustic monitoring (PAM) systems capable of classifying cetaceans at the species level. The utility of such systems depends on the systems' ability to operate across a wide range of ocean acoustic environments; however, anecdotal evidence suggests that site-specific propagation characteristics impact the performance of PAM systems. Variability in propagation characteristics leads to differences in how each cetacean vocalization is altered as it propagates along the source-receiver path. A propagation experiment was conducted in the Gulf of Mexico to investigate the range-dependent impacts of acoustic propagation on the performance of an automated classifier. Modified bowhead and humpback vocalizations were transmitted over ranges from 1 to 10 km. When the classifier was trained with signals collected near the sound source, it was found that the performance decreased with increasing transmission range-this appeared to be largely explained by decreasing signal-to-noise ratio (SNR). Generation of performance matrices showed that one method to develop a classifier that maintains high performance across many ranges is to include a varied assortment of ranges in the training data; however, if the training set is limited, it is best to train on relatively low SNR vocalizations.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".