Development of the Ergonomic Activity Sampling (EAS) Method to Analyse Video-Documented Work Processes with Activity Sampling
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".