Modelled and measured sound levels from a seismic survey in the Canadian Beaufort Sea
Bibliographic record
Abstract
JASCO Applied Sciences performed acoustic modeling and measurements to calculate marine mammal exclusion zones for Chevron Canada Limited’s 2012 Sirluaq 3-D seismic program in the Canadian Beaufort Sea. The Sirluaq survey was located in deep water (>650 m), on and beyond the continental slope, and presented unique challenges from both modeling and measurement standpoints. The modeling was performed with JASCO’s Marine Operations Noise Model (MONM), which uses a parabolic-equation-based algorithm to accurately predict N×2-D sound propagation in ocean environments. Sound levels were measured with five calibrated Autonomous Multichannel Acoustic Recorder (AMAR) systems at distances of 50–50 000 m from the airgun array, in water depths ranging from 50 to 1500 m. The sensors were laid out to capture sound levels in both the broadside (perpendicular to survey line) and endfire (along the survey line) directions. The high-resolution digital recordings of seismic sounds were analyzed to determine peak and rms sound pressure levels (SPL), and sound exposure levels (SEL) as functions of range from the airgun array. The model estimates were generally conservative; however, the model predictions at the specific depth of the receivers accurately predicted the existence of a shadow zone and the overall transmission loss trend.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".