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Record W1492898953 · doi:10.3233/wor-2009-0883

Improving a workstation in an existing cab by means of a participatory approach: The case of subway operators' workstations

2009· article· en· W1492898953 on OpenAlexafffund
Marie Bellemare, Sylvie Beaugrand, Christian Larue, Danièle Champoux

Bibliographic record

VenueWork · 2009
Typearticle
Languageen
FieldEngineering
TopicTraffic and Road Safety
Canadian institutionsInstitut de recherche Robert-Sauvé en santé et en sécurité du travailUniversité Laval
FundersInstitut de Recherche Robert-Sauvé en Santé et en Sécurité du Travail
KeywordsWorkstationOrder (exchange)Process (computing)Space (punctuation)Service (business)Citizen journalismEngineeringOperations managementProcess managementComputer scienceManufacturing engineeringEngineering managementTransport engineeringBusinessMechanical engineeringMarketingOperating systemWorld Wide Web

Abstract

fetched live from OpenAlex

A study was conducted to identify possible solutions for redesigning a subway cab in order to improve the posture of drivers working in a restricted space. The approach used included the participation of a working group comprised of operations, maintenance, and engineering managers as well as several drivers. After 6 meetings in which different simulation techniques were used, the working group proposed changes for increasing the available space inside the cab and three seat designs. The involvement of the actors from the three departments affected by the changes, as well as the operators, throughout the process, was a determining factor in the advancement and acceptance of the projects. The fact that 400 cars are currently in service and must be modified means that it will take several years to implement the modifications in the entire fleet.

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)0.008
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.008
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0120.006
Scholarly communication0.0030.003
Open science0.0020.004
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.028
GPT teacher head0.256
Teacher spread0.228 · 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

Citations1
Published2009
Admission routes2
Has abstractyes

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