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Record W2347063424

Response to Oil Sands Products Assessment

2015· article· en· W2347063424 on OpenAlexaboutno aff
Kurt Hansen, Mike Sprague, John Joeckel, Mark Rockley

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicOil Spill Detection and Mitigation
Canadian institutionsnot available
Fundersnot available
KeywordsOil refineryOil spillSAFEROil sandsPipeline transportCoast guardAsphaltEnvironmental scienceEmergency responsePetroleumEnvironmental planningRisk analysis (engineering)Waste managementEngineeringEnvironmental protectionBusinessEnvironmental engineeringGeographyGeologyComputer scienceComputer security
DOInot available

Abstract

fetched live from OpenAlex

Abstract : Domestic production of crude oil in North America has increased at a tremendous rate. Oil sands products (OSP), such as diluted bitumen (Dilbit) from Alberta, Canada, are subject to spilling during transport to domestic markets and refineries in the U.S. via pipeline, tank cars, or marine vessels. This report includes a qualitative risk assessment of potential spills of Dilbit and identifies initial response issues. Specifically, the following information, along with appropriate recommendations, are documented in this report: Geographic areas most at risk for spills and the prime routes of transportation in those areas. Techniques identified for response to surface oil and submerged oil spills that can address OSPs. Identification of what additional information the U.S. Coast Guard decision-makers need and what additional equipment or tactics responders need to prepare for Dilbit spills in waterways. How addressing these recommendations will provide more robust and safer response to future spills of Dilbit. A description of proposed tasks for future research efforts related to the recommendations.

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)0.006
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.016
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0110.002

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 head0.020
GPT teacher head0.272
Teacher spread0.252 · 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 designObservational
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
Published2015
Admission routes1
Has abstractyes

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