Results from scenarios of altered commercial vessel traffic density, speed and transit route
Bibliographic record
Abstract
Noise increases resulting from a seven-fold increase in tanker and tug transits related to the Trans Mountain Expansion (TMX) project, and the efficacy of slowdown and rerouting measures as mitigation were estimated using a vessel noise model. Potential acoustic impacts to southern resident killer whales (SRKW, Orcinus orca) were considered by examining communication and echolocation ranges (0.5-15 kHz and 15-100 kHz respectively) at typical swimming and foraging depths (7.5, 50 and 100 m) during May to October. Measure effectiveness was determined through the comparison of simulated scenarios to a pre-project baseline. Increases were focused in shipping lanes and shallow water. Slowing vessels to 10 knots throughout their transit was the most effective mitigation measure, whereas a lateral displacement of tugs through the Strait of Juan de Fuca made no change overall. The metric used to evaluate noise level change influenced conclusions on measure efficacy, where reductions were seen for slowdowns in upper (e.g., L75, L95) but not in lower percentiles (L50 or below). By targeting the faster moving vessels, slowdowns effectively shift the greatest noise sources to these lower levels. Results presented are from best available inputs at the time; amendments may occur as refinements are made to the model.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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 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".