Underwater Noise for Commercial Vessels: Develop a Plan before Finding a Treatment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Underwater noise is a growing concern for commercial vessels. As an example, efforts are already underway to reduce underwater noise and related impacts to Southern Resident Killer Whales (Orcas) in the Pacific Northwest. Groups like Transport Canada, Maritime Blue, Port of Vancouver, and others are investigating appropriate noise limits for vessels and strategies for ensuring noise levels are reduced. With this, there is a strong desire to identify treatments that can reduce underwater noise on existing vessels. Numerous “menu-style” lists of potential treatments have been compiled, but deciding which treatment to use is not trivial. Underwater noise reductions generally cannot be achieved through selection of a single treatment from a catalog. Rather than selecting treatments from a list, stakeholders should look to reduce underwater noise by choosing the right design approach. This approach would ideally be applied during design and construction phases, though post-construction design approaches can also be applied for existing vessels.
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.
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 | 0.001 | 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 | 0.002 | 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) | 0.003 | 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 it