Recent trends of vessel activities in proximity to cetaceans in the central Salish Sea
Bibliographic record
Abstract
NOAA Fisheries finalized new regulations in 2011 to protect endangered Southern Resident killer whales (Orcinus orca) (SRKW) by requiring vessels to remain at least 200 yards from all killer whales and to keep clear of the whales’ path. Washington State's Legislature followed suit in 2012. Vessel effects were identified as a risk factor for this small endangered population (presently 80 individuals) listed under the Endangered Species Act in the U.S. and the Species at Risk Act in Canada. The 2008 NOAA Recovery Plan for SRKW identified evaluating the need for regulations and/or protected areas as an important conservation action to minimize vessel effects. To inform development of the regulations, NOAA relied on data from the Soundwatch and Straitwatch programs, which monitor vessel activity around the whales. In the Salish Sea these groups observed over 1,000 incidents each summer from 2006-2013 when boaters were not in compliance with all laws and voluntary guidelines. In addition most incidents were committed by recreational boaters. Results from Soundwatch and Straitwatch data combined with scientific studies evaluating vessel impacts to whales contributed to developing protective regulations for an endangered species. The 2011 new U.S. federal regulation increased the viewing distance from a previous 100-yard guideline to a 200-yard rule and codified a previous guideline against positioning in the path of whales into a mandatory 400-yard rule. With new mandatory regulations in place, continued collection of Soundwatch and Straitwatch data is essential to evaluate the effectiveness of the new regulations in reducing vessel impacts. The combined rate of a vessel either within 100 yards of whales or parked in the path of whales increased from 1.2 per hour in 2006-2010 (pre-regulation) to 2.5 per hour in 2011-2013 (post-regulation). This preliminary analysis indicates that many boaters, particularly recreational boaters, are not complying with the new regulations. In 2013, WDFW had an elevated enforcement presence on the U.S. side of the Haro Strait region. Soundwatch data show that the three commercial ecotour vessel types had 2-3 times reduction in regulatory incident rates in the presence of WDFW enforcement compared to the absence of enforcement. Changes in boater behavior and improved compliance appear to be possible through increased education and enforcement.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".