Modeling vessel noise emissions through the accumulation and propagation of Automatic Identification System data
Bibliographic record
Abstract
Recent research has demonstrated the importance of soundscape characterization, modeling, and mapping with regard to their potential to highlight noise levels that can adversely affect fish behavior. Models and noise maps are seen as valuable tools for generating comprehensive information at relatively low costs; a model-based approach presents a powerful and cost-effective way to evaluate noise levels. This research aims to develop a vessel noise modeling method using Automatic Identification System (AIS) and online data. The vessel noise map is produced using estimated source levels of individual ships at each AIS transmission point along a vessel transit line. The accumulation and propagation of these transit line emissions, in 1 km grid squares, produces an ocean shipping noise map showing average received levels over the desired time period. The results show temporal and spatial differences in vessel noise emissions, with summer months nosier than winter months, and coastal areas and known shipping channels much nosier than the open ocean. Unlike many previous models, this approach uses individual vessel source emissions, and is very computationally efficient even for large datasets.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".