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Record W2513207392 · doi:10.1002/cjce.22622

CSChE chemical spill environmental/ecological risk assessment guideline development

2016· article· en· W2513207392 on OpenAlexaffvenueabout
Manny Marta

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2016
Typearticle
Languageen
FieldDecision Sciences
TopicRisk and Safety Analysis
Canadian institutionsPublic Safety Canada
Fundersnot available
KeywordsGuidelineScope (computer science)CredibilityStakeholderProcess (computing)BusinessRisk analysis (engineering)Government (linguistics)Product (mathematics)Risk assessmentEnvironmental resource managementChemical industryEnvironmental planningEnvironmental scienceEngineeringEnvironmental economicsComputer scienceEnvironmental engineering

Abstract

fetched live from OpenAlex

Abstract The Canadian Society for Chemical Engineering (CSChE) Process Safety Management (PSM) Division, with representation from the chemical and oil industry, government, and some other interested parties, concluded that there is no guideline commonly recognized by the chemical industries in Canada for conducting environmental/ecological risk assessments (ERA) involving postulated acute chemical/energy product spills from facilities of interest. These facilities would include those that produce, process, store, distribute, handle, or transport chemicals or energy products. This paper explains the need for this type of guideline in Canada. It will also describe the expertise and stakeholder representation involved in order to optimize credibility and acceptance. Furthermore, it will include information on a proposed scope, objectives, and content for a prospective environmental risk assessment guideline, with an exclusive focus on acute chemical spill events involving fixed and transportation sources. This guideline will also support elements of the proposed CSA Standard on Process Safety Management (PSM).

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)0.026
metaresearch head score (Gemma)0.034
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: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.670
Threshold uncertainty score0.663

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0260.034
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0070.004
Science and technology studies0.0040.002
Scholarly communication0.0040.001
Open science0.0090.003
Research integrity0.0070.004
Insufficient payload (model declined to judge)0.0070.004

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 head0.026
GPT teacher head0.274
Teacher spread0.247 · 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
GenreMethods

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

Citations1
Published2016
Admission routes3
Has abstractyes

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