MODELLING EXPOSURE OF MARINE MAMMALS TO UNDERWATER NOISE FROM PULSED SOURCES IN LONG-DURATION SURVEYS
Bibliographic record
Abstract
Sound sources, such as airgun arrays, used during exploratory seismic surveys for subsea hydrocarbon deposits are typically towed by a vessel. Multiple arrays and multiple vessels may be used and firing patterns amongst the arrays can be complicated. The animals exposed to these sound fields may move as well. In order to determine the potential impacts of the sounds on animals, a method is needed to estimate received sound levels. Realistic animal movement within the sound field can be simulated, and repeated random sampling (Monte Carlo)—achieved by simulating many animals within an area—used to estimate the sound exposure history of animals during a survey. Monte Carlo methods provide a heuristic approach to determine the probability distribution function (PDF) of complex situations, such as animals moving in a sound field. A greater number of random samples, in this case more simulated animals (animats), better approximates the PDF. In the early versions of this model development, Jasco utilized some modules from the ESME software workbench (Effects of Sound on the Marine Environment), a package made available by the Boston University Hearing Research Center and the Office of Naval Research, as well as Marine Mammal Movement and Behavior (3MB) model from the National Marine Mammal Foundation. After more experience was gained using ESME in production modelling work, Jasco found it advantageous to implement JEMS (Jasco Exposure Modelling System) a new, more specific program to interface between the animat tracks output by 3MB and the underwater noise field predictions of the Jasco acoustic propagation models, MONM (Marine Operations Noise Model). This paper will present the reasons why JEMS development was undertaken and an overview of how JEMS solves the exposure modelling problems.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".