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Record W1945804899 · doi:10.3233/wor-2011-1159

Evaluating the physical demands of three tarping systems for flatbed transport trailers

2011· article· en· W1945804899 on OpenAlexafffund
J. Marshall, R. W. Wells

Bibliographic record

VenueWork · 2011
Typearticle
Languageen
FieldMedicine
TopicMusculoskeletal pain and rehabilitation
Canadian institutionsUniversity of Waterloo
FundersWorkplace Safety and Insurance Board
KeywordsTruckSimulationRackAutomotive engineeringEngineeringComputer scienceTransport engineeringMechanical engineering

Abstract

fetched live from OpenAlex

OBJECTIVE: Tarping and untarping loads on flatbed trailers creates concerns related to falls as well as high musculoskeletal demands. The purpose of this study is to compare the demands and risks present when using three different tarping systems and to determine which system is preferred to reduce demand and injury risks. PARTICIPANTS: Nine male volunteers from a flatbed trucking company participated in the study. METHODS: The truck drivers covered the load on the flatbed trailer using three different tarping systems: manual tarps, sliders, and rack and tarp kits. Multiple measures were used to characterize the three tarping systems, including required forces, identifying injury risk by assessing peak, average and cumulative forces, moments and electromyography, heart rate, and exposure to fall hazards. RESULTS: Manual tarping resulted in greater physical demands and safety risks than the two alternate systems, both of which all participants preferred. The slider method was preferred overall as it has numerous advantages. CONCLUSIONS: The slider and rack and tarp kit methods offered a wide range of benefits including reduced physical demands, reduced exposure to fall hazards as well as improved productivity due to the shorter execution times, but had the disadvantage of being less versatile.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.555
Threshold uncertainty score0.166

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.090
GPT teacher head0.345
Teacher spread0.255 · 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

Citations3
Published2011
Admission routes2
Has abstractyes

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