An automated real-time vessel sound measurement system for calculating monopole source levels using a modified version of ANSI/ASA S12.64-2009.
Bibliographic record
Abstract
Underwater noise from vessels permeates many of the world’s oceans. While vessel sound emissions are at levels typically below those that would be acutely injurious to marine fauna, this noise can interfere with normal use of sounds, such as for prey and predator detection, socialization and mate attraction. Exposures to vessel noise over extended times, especially in key habitat areas, is likely to lead to chronic adverse effects including reduced feeding efficiency and difficulty finding mates. Assessments of the effects of shipping noise on marine fauna often use acoustic propagation models to predict the levels of sound exposure. These models require accurate vessel sound emission source levels. American National Standards Institute (ANSI) standard S12.64-2009 (reaffirmed in 2014) describes procedures for measuring underwater sound from ships. The standard deals with radiated noise level (RNL) source levels that assume 20 Log(r) transmission loss (TL) between the vessel positions and the measurement hydrophones. That approach does not account for interference from surface and seabed reflections. Most acoustic models directly account for these effects, and therefore require monopole source levels (MSL). MSL assumes all acoustic energy originates at a single point in the water, at a specified depth. Few fully systematic measurements of source levels of large commercial vessels are available. A few recent studies have published source levels obtained from large numbers of vessel passes, but those measurements are typically partly opportunistic, with hydrophones in shallow water or located several kilometers distance from the vessel paths. Also, most of the existing published measurements report RNL but not MSL. The Strait of Georgia Underwater Listening Station (ULS) and JASCO Applied Science’s PortListenTM processing software were designed to obtain systematic measurements of large numbers of vessels in relatively deep water (173 m). The system reports RNL measurements in approximate conformance with ANSI/ASA S12.64 (2009) Grade-A processing but with Grade-C geometry and noting only single vessel passes are acquired per transit. MSL calculations are made similarly but with a modified backpropagation method.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".