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Record W2894379995 · doi:10.23880/eoij-16000167

Development of the Ergonomic Activity Sampling (EAS) Method to Analyse Video-Documented Work Processes with Activity Sampling

2018· article· en· W2894379995 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueErgonomics International Journal · 2018
Typearticle
Languageen
FieldEngineering
TopicErgonomics and Human Factors
Canadian institutionsÉcole de Technologie Supérieure
FundersÉcole de technologie supérieure
KeywordsSampling (signal processing)Work (physics)Industrial engineeringHuman factors and ergonomicsExperience sampling methodComputer scienceManufacturing engineeringEngineeringPsychologyMechanical engineeringPoison controlComputer visionMedicineMedical emergencySocial psychology

Abstract

fetched live from OpenAlex

Ergonomics analyses examine design parameters of work processes, e.g. postures and movements or action forces, with the aim of assessing work systems or work processes with regard to feasibility and long-term tolerability. Numerous applications of ergonomics analysis at "normal" industrial and service workplaces can be found in the relevant literature as well as in practical field studies. In contrast, there are only a few methodical presentations of ergonomics analysis under critical working and environmental conditions, e.g. in fire brigade and medical emergency operations, in heat and cold environments, in radioactive contamination of workplaces, etc. With the EAS, a procedure for video-supported activity sampling analysis is presented. Based on case studies from aircraft de-icing, it is shown that video-based activity sampling studies allow a well-founded analysis of postures and movements with a cost-benefit ratio that is acceptable to the analyst.

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)0.001
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.600
Threshold uncertainty score0.883

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 science0.0010.000
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 head0.034
GPT teacher head0.305
Teacher spread0.270 · 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