Methods for Laboratory Replication of Rotary-Wing Air Ambulance Vibration Conditions for Neonatal Patient Transport Applications
Bibliographic record
Abstract
Neonatal patients in need of specialized care may require transport by rotary-wing air ambulances. These patients are subjected to environmental stressors during transport, including elevated levels of mechanical vibration. Aircraft vibration is transmitted through the transport system and incubator to the patient. The unique vibration profile is dependent on vehicle type and phase of flight. To improve safety for these patients, we aim to evaluate the vibration exposure across this complex system. The purpose of this paper is to present and evaluate the methods used for aircraft data collection and replication of aircraft vibration profiles in a laboratory setting. Our current focus is on neonatal transportation in Ontario, Canada, where Leonardo AW139 helicopters are used for patient transport. AW139 field data were collected and processed to generate laboratory excitation profiles for discrete phases of flight. The vehicle data were used to drive a series of laboratory shaker table experiments, in three axes, to evaluate the response of different configurations of the transport system. We present the methods used to simulate transport conditions, from vehicle data collection to laboratory shaker experimentation, and evaluate the behavior of the test apparatus. The simulated motion has been verified against the aircraft data to identify sources of error in the experimental setup. Some limitations in the shaker and control system present inherent differences in the input and response. However, it was found that the greatest spectral error occurred outside the frequency range of interest (>80 Hz) and that the shaker controller successfully replicates the energy levels recorded in the aircraft.
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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".