Identification of physically fatiguing tasks performed during aircraft open-basket ground de-icing activities
Bibliographic record
Abstract
BACKGROUND: Airplane de-icing technicians work from either an open-basket or closed-basket. OBJECTIVE: The objective of this study is to identify the tasks that have an influence on the physical fatigue of open-basket aircraft de-icing technicians. METHODS: In a Canadian airport during the winter of 2016-2017, a field study was conducted in which the heart rate of 12 volunteer participants was collected. The data was analyzed along with the 22 tasks that make up the activity of open-basket aircraft de-icing. For each participant, the mean absolute cardiac cost per task was compared. The evolution of the cardiac signal based on the resting heart rate and steady state limit was also characterized. RESULTS: According to the cumulative results fatigue occurs for periodic tasks as well as double tasks. More precisely, the most physically fatiguing tasks are spraying de-icing and anti-icing fluids, moving the basket and truck, as well as tactile control and de-icing quality control at ground level. CONCLUSIONS: Similar studies would need to be conducted in other aircraft de-icing facilities to improve the generalization of the results.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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 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".