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Record W2937188115 · doi:10.29173/mocs28

Recovery time analysis of back muscle fatigue in panelized residential modular construction factory

2016· article· en· W2937188115 on OpenAlexafffundvenueabout
Sangjun Ahn, SangUk Han, Mohamed Al‐Hussein

Bibliographic record

VenueModular and Offsite Construction (MOC) Summit Proceedings · 2016
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFactory (object-oriented programming)ScheduleRest (music)Working timeOperations managementWork (physics)EngineeringRisk analysis (engineering)Computer scienceMedicineMechanical engineering

Abstract

fetched live from OpenAlex

Construction workers in panelized construction factory settings are often exposed to physically demanding and repetitive activities during panel assembly, which requires manual processes. Consequently, these factory workers are often exposed to potential risks, namely work-related musculoskeletal disorders (WMSDs), due to muscle fatigue. Therefore, it is important to evaluate levels of fatigue and provide appropriate interventions to minimize the health risk of workers. Previous studies have shown that sufficient rest could reduce risk of WMSDs from fatigue and is considered one of most practical way to minimize risk. Rest break schedule analysis on workeräó»s fatigue in different industries has been documented; however, to the authoräó»s knowledge, the analysis on panelized construction has not yet been studied. To address this gap, fatigue of workers is estimated using an equation, which was derived mathematically, in terms of recovery time compared with break time schedule. A case study of a panelized construction factory in Edmonton, Alberta, Canada is performed to evaluate the effectiveness of rest break schedules. The results from the case study show that workers at the panelized construction factory require more frequent and longer break time to reduce potential risk of WMSDs.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.091
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.043
GPT teacher head0.350
Teacher spread0.307 · 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.

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
Published2016
Admission routes4
Has abstractyes

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