Bibliographic record
Abstract
The culture, economy and fish and wildlife would all be dramatically impacted by an oil spill in the Salish Sea. With multiple projects proposed that will expand coal and oil exports from ports in British Columbia and Washington, oil spill risks are escalating rapidly. This unprecedented level of additional vessel traffic, primarily fossil fuel transports, significantly increases the risks of a major oil spill. In 2010, there were 11,000 deep draft vessel transits through the Strait of Juan de Fuca. Around 4,300 of these are destined for United States’ ports in Puget Sound. The other 6,250 make for Canadian ports. Past projections identified 1,322 oil tankers, each of which carries an average of 30 to 40 million gallons of crude oil. This level of shipping traffic already comes with a certain inherent level of risk. Currently, around 12,394 large vessels and oil barges transit past the San Juan Islands each year. A Vessel Traffic Risk Assessment for Northern Puget Sound and the Strait of Juan de Fuca (VTRA 2014) found that if all proposed projects were approved, vessel traffic would increase by 21%, accident frequency by 18%, and oil spill loss by 68%. Since the VTRA projections, there has been a sea change in the number of ships, types of products, and political climate for marine shipping through 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 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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".