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Record W3043806097 · doi:10.33439/ergonomi.743276

THE ERGONOMIC RISK ANALYSIS WITH REBA METHOD IN PRODUCTION LINE

2020· article· en· W3043806097 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueErgonomi · 2020
Typearticle
Languageen
FieldEngineering
TopicErgonomics and Human Factors
Canadian institutionsYukon University
Fundersnot available
KeywordsHuman factors and ergonomicsProduction (economics)Production lineWork (physics)Multidisciplinary approachIdleOperations managementRisk analysis (engineering)Computer scienceEngineeringBusinessPoison controlMedicineEnvironmental healthMechanical engineering

Abstract

fetched live from OpenAlex

Ergonomics is a group of multidisciplinary studies that investigate and improve the compatibility of humans with the machine and the environment by examining the physical, environmental and psychological risk factors. The primary purpose of ergonomics is to ensure employee health and safety, and increase work efficiency (such as reduced idle capacity, increased production, increased product quality). Because employing workers in a healthy and safe condition enables an increase in work efficiency. In this study, ergonomic risk analysis was selected on the production line of an enterprise with the REBA method and suggestions for improvement were included. The working cluster consists of 30 unskilled workers on the production line. As a result of the analysis, 66.6% of the production process is at medium risk and 33.4% is at high risk. After the improvement works to be done, it is expected that ergonomic risks would reduce and an increase in production and efficiency.

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.

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 score0.334
Threshold uncertainty score0.469

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 head0.009
GPT teacher head0.203
Teacher spread0.194 · 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