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Record W2736097260

Projected Vessel Traffic Increase in the Salish Sea

2017· article· en· W2736097260 on OpenAlexaboutno aff
Stephanie Buffum

Bibliographic record

VenueWestern CEDAR (Western Washington University) · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicMaritime Security and History
Canadian institutionsnot available
Fundersnot available
KeywordsGeographyEnvironmental science
DOInot available

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.863
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0020.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.030
GPT teacher head0.269
Teacher spread0.239 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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