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Record W2125884821 · doi:10.7901/2169-3358-2014.1.299

CANADA – UNITED STATES (SALISH SEA) SPILL RESPONSE ORGANIZATIONS: A COMPARISON

2014· article· en· W2125884821 on OpenAlexaffabout
Scott Knutson, Craig Dougans, Gary A. Reiter, Don Rodden, Erik Kidd

Bibliographic record

VenueInternational Oil Spill Conference Proceedings · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicOil Spill Detection and Mitigation
Canadian institutionsTransport Canada
Fundersnot available
KeywordsCoast guardJurisdictionContingency planTerritorial watersOil spillGeographyPolitical scienceEnvironmental planningEnvironmental protectionEnvironmental resource managementInternational lawLawEnvironmental scienceManagement

Abstract

fetched live from OpenAlex

ABSTRACT The Salish Sea comprises the inland marine waters of Washington and British Columbia and is intersected by an international border between Canada and the United States. Planning for oil spills that threaten to cross the international border is under the jurisdiction of the Canadian Coast Guard and the United States Coast Guard as described in the Canada-United States Joint Marine Contingency Plan. As Canadian companies gain approval to construct new pipelines in order to move oil sands from Alberta, Canada, to Vancouver, British Columbia, and westward, governments, agencies and citizens are publicly questioning whether current levels of oil spill preparedness and response equipment will be adequate for the increased tanker traffic from Canadian ports. This paper will be a single document that contains a snapshot of regulations, actual inventories and current philosophies that make up the 2014 response picture for the Salish Sea. It does not seek to denigrate either nation's response posture but rather to provide hard numbers as a common foundation for future discussions.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.052
Threshold uncertainty score0.380

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0070.015
Science and technology studies0.0030.002
Scholarly communication0.0040.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.001

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.

Opus teacher head0.009
GPT teacher head0.228
Teacher spread0.219 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2014
Admission routes2
Has abstractyes

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