A functional regression analysis of vessel source level measurements from the Enhancing Cetacean Habitat and Observation (ECHO) database
Bibliographic record
Abstract
Measurements of the source levels of 9880 passes of 3188 different large commercial ships from the Enhancing Cetacean Habitat and Observation (ECHO) program database were used to investigate the dependencies of vessel underwater noise emissions on several vessel design parameters and operating conditions. Trends in the dataset were analyzed using functional regression analysis, which is an extension of standard regression analysis and represents a response variable (decidecade band source level) as a continuous function of a predictor variable (frequency). The statistical model was applied to source level data for six vessel categories: cruise ships, container ships, bulk carriers, tankers, tugs, and vehicle carriers. Depending on the frequency band and category, the functional regression model explained approximately 25%-50% of the variance in the ECHO dataset. The two main operational parameters, speed through water and actual draft, were the predictors most strongly correlated with source levels in all of the vessel categories. Vessel size (represented via length overall) was the design parameter with the strongest correlation to underwater radiated noise for three categories of vessels (bulkers, containers, and tankers). Other design parameters that were investigated (engine revolutions per minute, engine power, design speed, and vessel age) had weaker but nonetheless significant correlations with source levels.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".