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
Predicting neck response and injury resulting from motor vehicle accidents is essential to improving occupant protection. A detailed human cervical spine finite element model has been developed, with material properties and geometry determined a priori of any validation, for the evaluation of global kinematics and tissue-level response. Model validation was based on flexion/extension response at the segment level, tension response of the whole ligamentous cervical spine, head kinematic response from volunteer frontal impacts, and soft tissue response from cadaveric whole cervical spine frontal impacts. The validation responses were rated as 0.79, assessed using advanced cross-correlation analysis, indicating the model exhibits good biofidelity. The model was then used to evaluate soft tissue response in frontal impact scenarios ranging from 8G to 22G in severity. Disc strains were highest in the C4-C5-C6 segments, and ligament strains were greatest in the ISL and LF ligaments. Both ligament and disc fiber strain levels exceeded the failure tolerances in the 22G case, in agreement with existing data. This study demonstrated that a cervical spine model can be developed at the tissue level and provide accurate biofidelic kinematic and local tissue response, leading to injury prediction in automotive crash scenarios.
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 | 0.000 | 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) | 0.001 | 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