Bibliographic record
Abstract
High Speed Craft (HSC) occupants can be exposed to eccentric slam impacts of up to 20 g as a result of hull separation from the water during routine operation, subjecting the occupants to lateral, longitudinal, and torsional loading in addition to the primary vertical loading.These impacts have been the source of acute and chronic spinal injury for occupants, as well as causing motion-induced fatigue and reduced situational awareness.Current research focuses on mitigating these adverse effects by the use of suspension seating on HSC.Development and validation of an accurate mathematical model of HSC suspension seating with the ability to optimize seat design parameters is required for efficient seat analysis, design, and tuning.A Newton-Euler approach was used to develop a general two-degree-of-freedom (DOF) spatial dynamic model of the seat-occupant system with the primary purpose of accurately predicting the occupant's vertical response to 6DOF input base motion.Coupling this approach with a modular coding implementation that allows for interchangeable sub-models of seat components engenders flexibility in application and a clear connection between the model parameters and the physical system.The model was validated against uni-directional, single impact experimental data and available 6DOF HSC data and found to adequately predict the occupant's vertical acceleration when using the stiffness and damping characteristics from component testing of the seat of interest.This model provides a flexible base for future examination of suspension seating on HSC and the effect of various parameters thereon.' New axis after counterclockwise rotation through φ 1 " New axis after counterclockwise rotations through φ 1 and φ 2 ˙First derivative with respect to time ¨Second derivative with respect to time ˜Skew-symmetric matrix of vector GLO Global frame IN Inertial frame LO Local frame MO Model frame
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".