Application of a seismic network to baleen whale call detection and localization in the Panama basin–a Bryde's whale example
Bibliographic record
Abstract
Baleen whales use sounds of various characteristics for different tasks and interactions. This study focuses on recordings from the Costa Rica Rift, in the Eastern Tropical Pacific Ocean, made by 25 ocean-bottom seismographs and a vertical array of 12 hydrophones between January and February 2015. The whale calls observed are of two kinds: more commonly, repetitive 4-5 s-long signals separated into two frequency bands centered at ∼20 and ∼36 Hz; less commonly, a series of ∼0.5 to 1.0 s-long, lower amplitude signals with frequencies between 80 and 160 Hz. These characteristics are similar to calls attributed to Bryde's whales which are occasionally sighted in this region. In this study, the repetitive calls are detected using both the short-term average/long-term average approach and a network empirical subspace detector. In total, 188 and 1891 calls are obtained for each method, demonstrating the value of the subspace detector for highly similar signals. These signals are first localized using a non-linear grid search algorithm and then further relocalized using the double-difference technique. The high-resolution localizations reveal the presence of at least seven whales during the recording period, often crossing the instrument network from southwest to northeast.
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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 | 0.001 |
| 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".