Tracking baleen whale calls in the Lower St. Lawrence Seaway, Canada, using land seismometers  
Bibliographic record
Abstract
The Lower St. Lawrence Seaway (LSLS) is part of a major marine shipping corridor in eastern Canada, and also an essential feeding ground for fin whales and blue whales. Understanding the whale migration and habitat usage in the LSLS is critical for informing conservation policies that minimize noise pollution and risk of collision to the whale populations. In this study we utilize continuous recordings of six broadband seismometers located on the north and south shores of the St. Lawrence River to characterize the frequency range, recurrence interval and duration of fin and blue whale calls. We further use the whale call detections to quantify their spatial and temporal variations along the LSLS between February 2020 and January 2022, with the caveat that the detection range at these land stations is probably limited to a few kilometers due to energy loss along the seismic wave travel paths through multiple interfaces. We identified higher whale call detection rates at stations near the northwest of St. Lawrence Gulf than the upstream Estuary, suggesting possible influences of ocean currents and ice conditions. Whale calls are detected year around, with majority in the fall/winter months (September to February), implying seasonal and annual variations that may be influenced by climate change. We are currently analyzing recordings from a temporary deployment of 48 nodal seismometers, at 10-km average spacing, along the shorelines of the LSLS between September-October 2023, to further quantify the spatial patterns of whale calls and identify possible linkages to coastal bathymetry, ocean currents and preferential diets for the baleen whales.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".