Ergonomic Time and Motion Studies of Aircraft De-icing Work
Bibliographic record
Abstract
This paper reports results of time and motion studies and ergonomic consequences of aircraft de-icing work. De-icing of aircraft on the ground is extremely important for maximizing technical safety, but also a major challenge for persons performing it. Between December 2016 and March 2017 we carried out video-supported time and motion studies on 11 personnel performing de-icing work in open baskets at a Canadian airport. Total time analyzed was 788 min, during which 1192 individual observations were made. Our observation sessions varied in length from 59 to 96 min, partly for weather reasons. After ascertaining the work systems used by the de-icers and determining the principal factors influencing them, we used REFA methods to perform a hierarchical analysis of work activities. Energy turnovers generated by these activities were calculated. These lie between 4 and 13 kJ/min, depending on weather conditions, air traffic density and individual work patterns of the de-icing personnel. Contributions of the individual activities to total value added were classified. Roughly one third of the de-icing activities made a direct contribution. The remaining two thirds made only indirect contributions or none at all. Stresses arising from the work were compared with the few findings reported in the literature. Further investigations are needed to understand thoroughly centralized de-icing activities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".