Low frequency baleen whale calls detected on ocean-bottom seismometers in the Lau basin, southwest Pacific Ocean
Bibliographic record
Abstract
Ten months of broadband seismic data, recorded on six ocean-bottom seismographs located in the Lau Basin, were examined to identify baleen whale species. As the first systematic survey of baleen whales in this part of the southwest Pacific Ocean, this study reveals the variety of species present and their temporal occurrence in and near the basin. Baleen whales produce species-specific low frequency calls that can be identified by distinct patterns in data spectrograms. By matching spectrograms with published accounts, fin, Bryde's, Antarctic blue, and New Zealand blue whale calls were identified. Probable whale sounds that could not be matched to published spectrograms, as well as non-biologic sounds that are likely of volcanogenic origin, were also recorded. Detections of fin whale calls (mid-June to mid-October) and blue whale calls (June through September) suggest that these species migrate through the region seasonally. Detections of Bryde's whale calls (primarily February to June, but also other times of the year) suggest this species resides around the basin nearly year round. The discovery of previously unpublished call types emphasizes the limited knowledge of the full call repertoires of baleen whales and the utility of using seismic survey data to enhance understanding in understudied regions.
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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 | 0.000 |
| 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) | 0.001 | 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".