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Record W3025953019 · doi:10.31873/ijetr.9.12.41

Electric Power Industry Accident Prevention in Canada

2019· article· en· W3025953019 on OpenAlexaboutno aff
Ganesh Narine

Bibliographic record

VenueInternational Journal of Engineering and Technical Research (IJETR) · 2019
Typearticle
Languageen
FieldDecision Sciences
TopicRisk and Safety Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsAccident (philosophy)Electric power industryElectric powerBusinessPower (physics)EngineeringElectrical engineeringElectricityPhysics

Abstract

fetched live from OpenAlex

The Canadian electric power industry is a sophisticated first-world system that forms integral and critical ties on the North American electric power grid. The availability of safe, reliable, high-quality electric power is vitally important for customers in Canada, a country with extreme variations in environmental conditions and geographical reach. The purpose of this study is to prevent accidents where Canadian electric power industry workers become seriously or fatally injured. The study, conducted on Survey Monkey and facilitated over a four-round Delphi exercise, involved nine participants who were a panel from the Canadian electric power industry. The participants provided feasible, desirable, important solutions that they were confident will or can prevent future workplace accidents and at the same time, serious or fatal injuries to workers. Participants in this study provide twenty different solutions.

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)0.004
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.269
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.060
GPT teacher head0.421
Teacher spread0.361 · 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.

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

Citations2
Published2019
Admission routes1
Has abstractyes

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