Multi-Body Dynamic Analysis of Cervical Spine for Helicopter Pilots
Bibliographic record
Abstract
Helicopter pilots use helmets equipped with night vision goggle and counter weight. This increased load can lead to disc injury, so it is necessary to evaluate the load and moments applied to each cervical disc when pilot head is moving in different flight conditions. A 3D multi-body dynamic model of cervical spine is provided to investigate the effect of weight of the helmet in flexion, extension, lateral bending and axial rotation of the spine. The whole study was done in several steps: 1) to develop a non-linear dynamic model of spine. 2) to validate the model against the published data under flexion, extension, lateral bending and torsinal moments. 3) to solve three case studies to simulate a moving head in different direction. 4) to run the simulations again with consideration of adding a helmet into the model with different weight to find out the effects on the cervical discs loading. The results demonstrate that C2C3, C4C5 and C7T1 carry the highest loads depending on direction of imposed displacement on the head. Experts in the area of neck injury can study the results and locate the regions at risk of injury or they can feed this information into FEA model to get stress distribution in discs, bones or ligaments.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 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".