Leaders and Laggards: Dumping from Vessels in the Salish Sea, why it's concerning, and who is leading and lagging in preventing it.
Bibliographic record
Abstract
Shipping and Large Vessel Traffic doesn’t just pose a risk to ecosystems when something catastrophic happens, but normal operations are creating pollution that is harmful to marine life, including Southern Resident Killer Whales (SRKW). While some jurisdictions in the Salish Sea are tackling this head on, working in collaboration with industry, and prioritising pollution prevention, others are allowing themselves to become a relative dumping ground. In this panel a group of experts including researchers, campaigners, and government officials will discuss and share some of the most concerning aspects of 3 voluminous waste streams, as well as examples of jurisdictions leading and lagging on addressing them. The panelists will focus on raw sewage, raw greywater, and exhaust gas cleaning system (scrubbers) waste streams. Sewage and greywater contain Personal Care Products and Pharmaceuticals, on top of nutrients and pathogens, which are detrimental to SRKW and Chinook Salmon. Vessels, in particular cruise ships, produce gallons per passenger per day. While Puget Sound is well protected from sewage and greywater dumping, once the border with Canada is crossed, greywater doesn’t need to be treated by ships built before 2013 at all. Scrubbers create and dump acidic and toxic waste streams into the ocean. Contributing directly to ocean acidification and toxicity of sediment and water while locking in the use of heavy fuel oil. The Port of Seattle has successfully negotiated a pause on this type of dumping from ships, while they work in partnership with CLIA and the Washington Department of Ecology to study scrubber discharge impacts in the local area. While the Port of Vancouver is the fourth most dumped upon port in the world and currently has no protective bans in place.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".