Government of Canada: Reducing Vessel Noise and Disturbance
Bibliographic record
Abstract
This presentation describes Canada's comprehensive approach to reducing underwater radiated noise (URN) from ships, as well as some of Canada's national and international efforts to reduce and tackle the URN issue. One of the goals of these efforts is to better understand and manage the cumulative effects of shipping activities on endangered whales in different parts of the country, particularly the Southern Resident Killer Whale on our West Coast. Given the complexity of reducing underwater noise and physical disturbance from ships, the Government of Canada has taken a multidimensional approach to this issue. This approach includes both operational and technical solutions, takes into account the impacts and contributions of vessels of all sizes, supports ongoing research and development, and recognizes the importance of international engagement and collaboration in order to advance the knowledge, design and technologies of silent vessels. This presentation provides examples of initiatives that Canada has carried out as part of this multidimensional approach. This work is all part of a larger strategy to reduce physical and acoustic disturbance from vessels and work towards protection and recovery of Southern Resident Killer Whales, creating a quieter future for the whales in the Salish Sea.
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 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 | 0.000 | 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 | 0.000 | 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) | 0.000 | 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 teacher head, 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".