Bibliographic record
Abstract
<p>This undergraduate thesis pertains to the design of a parameterized tool intended for the modelling of business aircraft seat backrests. Backrests of seats vary in shape and size as per varying designs for optimal comfortability based on studies of spinal anthropometry. The overall study of backrest comfortability is of great significance due to the role it plays in the marketability and revenue generation of a business airlines. One of the ways in which backrest comfortability can be evaluated is through the biomechanical analysis of the pressure distributions amongst the surface. Hence, a parametric tool was developed as a means to provide a modular method for easily generating a geometric backrest surface and corresponding point cloud. This was achieved by determining prominent input parameters in a simplistic manner based on a literature review, developing a feasible methodology to define the backrest shape based on the inputs, and generating a corresponding three-dimensional fit of the backrest surface. As a means of validation, the tool was tested to generate backrest surfaces as per reference data, and comparisons were drawn based on error analysis between the actual surface and the tool-generated surface. Although the developed tool is not as robust as can be at this point in time, based on research of existing business aircraft seat backrests, it is able to provide fairly accurate geometric surfaces within a reasonable realm of user-inputted parameters. Contained in this report is an overview of the parametric tool design philosophy, a thorough description of its methodology, and error analysis with respect to reference data, as well as the corresponding MATLAB code(s) contained in the appendices.</p>
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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 | 0.000 | 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) | 0.002 | 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 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".