A multivariate analysis of vessel source levels from the enhancing Cetacean habitat observation (ECHO) ship noise database
Bibliographic record
Abstract
The Vancouver Fraser Port Authority’s Enhancing Cetacean Habitat Observation (ECHO) program acquired a large database of several thousand systematic commercial ship noise measurements between September 2015 and April 2017. These measurements were used to develop a multi-variate linear regression model of vessel source levels against several parameters that describe vessels and their measurement conditions. Covariates in the multi-variate model included ship category, ship length, dead-weight-tonnage, static draught, effective wind speed magnitude and direction, ship speed, and surface angle. The regression analysis examined the statistical significance of each covariate’s regression coefficient and produced a set of fit coefficients, one for each covariate for each frequency band. The multi-variate model was used to normalize the measurements for each category, and the remaining data variance reflected vessel-specific differences in noise emissions that could not be attributed to measurement circumstances. The multivariate analysis produced a powerful ship noise model that can predict decidecade band monopole source level (MSL) and radiated noise level (RNL), which is useful for understanding noise emissions variations with ship characteristics and under different operating conditions. [This research was funded by Transport Canada.]
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".