MétaCan
Menu
Back to cohort
Record W3118472921 · doi:10.18280/ijsse.100610

Analysis of Dangerous Conditions and Actions of the Painting Process

2020· article· en· W3118472921 on OpenAlexvenueno aff
Tri Ngudi Wiyatno, Fibi Eko Putra, Muhammad Aldi Albana, Tri Handoyo, Muhammad Rizki Oktavian, Putri Nika Andini Hidayat

Bibliographic record

VenueInternational Journal of Safety and Security Engineering · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicManagement and Optimization Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsHazardous wasteOccupational safety and healthWork (physics)Risk analysis (engineering)Production (economics)Process safety managementProcess (computing)Personal protective equipmentHuman healthWork safetyProperty (philosophy)Forensic engineeringBusinessEnvironmental healthEngineeringComputer scienceWaste managementMedicine

Abstract

fetched live from OpenAlex

Occupational health and safety is one of the most important issues in a company which is an important subject that has attracted a lot of attention in recent years. Work safety management system is the effort shown to the elements in production (human, equipment, materials and work environment), so that peaceful production activities can be realized and produce products that do not endanger the safety and health of workers. This is due to the interaction of elements in the production system in the form of death, serious injury, human injury, property damage and cessation of process loss. Primary data collection is done by distributing questionnaires to employees to record hazardous actions and hazardous conditions that are the direct cause of accidents resulting in serious injury and property damage or endangering workers and employees. The overall value of hazardous actions is 37% and hazardous conditions 24% still have a small effect that triggers the occurrence. OSHA measurement values prove that accidents, loss of time and other losses can be analyzed with the results of FR and SR values.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.396
Threshold uncertainty score0.169

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.010
GPT teacher head0.229
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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Safety and Security EngineeringSame topicManagement and Optimization TechniquesFrench-language works237,207