Bibliographic record
Abstract
The work presented in this report is to help develop and complete the methodology that can quickly predict the pressure distribution and estimate the comfortability of the aircraft seat. For this thesis, the human back and spine are introduced and modelled as the focus is only on the backrest of the aircraft seat. The bio modelling of the back and spine consists of the geometry including the spine curvature and back shape at various conditions. The variables include the body type of the sitter, the sitting posture and the backrest recline angle. Multiple cases of the body condition combining the these mentioned variables were modelled, which generates a comparatively inclusive human model for the future work of pressure distribution analysis. The initial building of the spine curve is based on an existing spine data, and the back shape is captured by experiments using 3D scanning technology. Forces acting on the spine are also obtained as a part of the modelling. With this more complete the inclusive bio model of the body, the pattern of the contact and pressure can then determine a more efficient configuration of cushion or aircraft seat innovation and design to achieve a better comfort.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".