Analysis and modeling of 255 source levels of merchant ships from an acoustic observatory along St. Lawrence Seaway
Bibliographic record
Abstract
An ensemble of 255 spectral source levels (SSLs) of merchant ships were measured with an opportunistic seaway acoustic observatory adhering to the American National Standards Institute/Acoustical Society of America S12.64-2009 standard as much as possible, and deployed in the 350-m deep lower St. Lawrence Seaway in eastern Canada. The estimated SSLs were sensitive to the transmission loss model. The best transmission loss model at the three measuring depths was an empirical in situ function for ranges larger than 300 m, fused with estimates from a wavenumber integration propagation model fed with inverted local geoacoustic properties for [300 to 1 m] ranges. Resulting SSLs still showed a high variability. Uni- and multi-variate analyses showed weak intermingled relations with ship type, length, breadth, draught, speed, age, and other variables. Cluster analyses distinguished six different SSL patterns, which did not correspond to distinctive physical characteristics of the ships. The broadband [20-500 Hz] source levels varied by 30 dB or more within all four 50-m length categories. Common SSL models based on frequency, length and speed failed to unbiasly replicate the observations. This article presents unbiased SSL models that explain 75%-88% of the variance using frequency, ship speed, and three other automatic identification system ship characteristics.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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 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".