Time Series of Underwater Noise at the MARS Station in the St. Lawrence Estuary (2021-2023)
Bibliographic record
Abstract
This dataset contains the time series of the spectra of acoustic recordings obtained at the MARS station from 2021 to 2023. The MARS station is composed of 12 hydrophones (underwater microphones) distributed at depths of 80, 173 and 300m over four vertical moorings. These hydrophones record continuously several months a year near shipping lanes in the St. Lawrence Estuary off the coast of Rimouski. The sample rate is 16 kHz (16000 samples per second) with a few short periods at 128 kHz for the years 2021 and 2022 and 32 kHz in 2023. The hydrophones used are GeoSpectrum M36-100s mounted on Aural-M3 recorders designed by Multi-Électronique. The PyPAM library was used to produce the dataset. The signal from the acoustic recordings was converted into spectra (acoustic levels depending on frequency) covering 1 minute each. These were transformed into milli-decade hybrid spectra (with a reduced number of bands at high frequency) and median spectra of the latter were obtained on 1-hour periods. These time series make it possible to study the evolution of ambient noise mainly coming from the maritime traffic, but also from the geophony (noise from wind and waves) and the biophony (sounds produced by the marine species). 1-minute time series in milli-decade hybrid and the accurate position of the moorings are available on request. This was carried out as part of the MARS project whose aim is to study the noise radiated by the maritime traffic and to propose mitigation methods. The MARS project is co-led by the Institut des sciences de la mer (ISMER) of the Université du Québec à Rimouski (UQAR) and Innovation maritime (IMAR), with the support of MTE Instruments and OpDAQ Systems. It involves a partnership with the shipowners Algoma Central Corporation, CSL, Desgagnés, and Fednav, and is financially supported by Transport Canada, the Quebec Ministry of Economy and Innovation and the St. Lawrence Economic Development Council (SODES).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".