Comparison of Different Parameters of Heart Rate Frequency of Tasks Performed during Aircraft Open-Basket Ground Deicing Activities
Bibliographic record
Abstract
Plane deicing is mandatory to insure safe plane take-off. Previous human factors studies have shown that open-basket deicing activity can be improved. The objective of the paper is to compare heart rate assessment models within a field study with numerous influencing variables and small sample size as well as to deepen our understanding of the most demanding openbasket tasks using cardiac output. A field study in a Canadian centered plane deicing facility was conducted in 2016-2017. 12 participants contributed to a thorough description and analysis of open-basket deicing activities. Respiratory and cardiac output of these participants was collected using Hexoskin vests. Working heart rate, heart rate reserves as well as calculations of absolute cardiac cost were done. Working heart rate (WHR), Heart Rate Reserve (HRR) and Absolute Cardiac Cost (ACC) do not behave uniformly for the majority of participants. In field studies with a large number of influencing variables on the heart rate, it is usually not sufficient to consider one single evaluation measure like WHR. In the interest of protecting employees, it seems to make sense to use the more cautious measures HRR or ACC as parameters instead of WHR. Superimposed activities (e.g. forced postures and dynamic use of upper body) have a significant effect on heart rate increases. In 8 out of 11 cases we have fatigue-related increases in heart rate over the observation period. Similar studies need to be conducted in other aircraft deicing facilities.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".