An investigation of dimensional scaling using cervical spine motion segment finite element models
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The paucity of experimental data for validating computational models of different statures underscores the need for appropriate scaling methods so that models can be verified and validated using experimental data. Scaling was investigated using 50th percentile male (M50) and 5th percentile female (F05) cervical spine motion segment (C4-C5) finite element models subject to tension, flexion, and extension loading. Two approaches were undertaken: geometric scaling of the models to investigate size effects (volumetric scaling) and scaling of the force-displacement or moment-angle model results (data scaling). Three sets of scale factors were considered: global (body mass), regional (neck dimensions), and local (segment tissue dimensions). Volumetric scaling of the segment models from M50 to F05, and vice versa, produced correlations that were good or excellent in both tension and flexion (0.825-0.991); however, less agreement was found in extension (0.550-0.569). The reduced correlation in extension was attributed to variations in shape between the models leading to nonlinear effects such as different time to contact for the facet joints and posterior processes. Data scaling of the responses between the M50 and F05 models produced similar trends to volumetric scaling, with marginally greater correlations. Overall, the local tissue level and neck region level scale factors produced better correlations than the traditional global scaling. The scaling methods work well for a given subject, but are limited in applicability between subjects with different morphology, where nonlinear effects may dominate the response.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| 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 it