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Record W2767535624 · doi:10.1002/prs.11942

Learning process safety principles through practice

2017· article· en· W2767535624 on OpenAlexafffund
M.Y. Gunasekera, Faisal Khan, Salim Ahmed

Bibliographic record

VenueProcess Safety Progress · 2017
Typearticle
Languageen
FieldChemical Engineering
TopicChemical Safety and Risk Management
Canadian institutionsMemorial University of Newfoundland
FundersNatural Sciences and Engineering Research Council of CanadaAssociation of Commonwealth Universities
KeywordsProcess safetyProcess safety managementProcess (computing)Promotion (chess)Laboratory safetySafety cultureEngineeringHazard analysisHazardSafety engineeringWork (physics)System safetyInherent safetyHierarchyWork in processRisk analysis (engineering)Process managementComputer scienceOperations managementReliability engineeringBusinessMedicineManagementMechanical engineering

Abstract

fetched live from OpenAlex

Learning of process safety principles through practice is an area that needs promotion in process and chemical engineering academic institutions. This work presents a problem‐based learning (PBL) activity to introduce process engineering safety protocols into laboratory experimental procedures. The activities include direct engagement of students in identifying hazards involved with each step in the experimental procedure and respective safety options related to each hazard. The required safety options are then selected in the hierarchy from inherent safety to procedural safety. The selected safety strategies are implemented and practiced during the experiment promoting industrial safety culture within the laboratory. This learning activity was introduced in the laboratory experimental procedure for evaluating the performance of a plug flow reactor. The feedback from students shows that the majority has a positive perception toward the main learning outcomes. © 2017 American Institute of Chemical Engineers Process Process Saf Prog 37:347–354, 2018

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)0.007
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.037

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0010.003
Scholarly communication0.0040.004
Open science0.0010.004
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.002

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 head0.023
GPT teacher head0.320
Teacher spread0.298 · 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 designQualitative
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

Citations5
Published2017
Admission routes2
Has abstractyes

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