Bibliographic record
Abstract
The stability of the human spine in compression has attracted a considerable amount of attention in recent years. The passive ligamentous thoracolumbar and lumbar spines are known to exhibit large displacements or hypermobility (i.e., instability in an imperfect column) under compression loads <100N. Since such compression loads are only a small fraction of those supported by the spine even in regular daily activities, let aside the manual material handling tasks, the question arises as to how the spine is stablized in vivo? Various stabilizing mechanisms have been proposed and investigated; wrapping loading [Shirazi-Adl and Parnianpour, 2000], postural adaptations [Shirazi-Adl and parnianpor, 1999], intra-abdominal pressure [Cholewicki et al, 1999] and muscle activation/coactivation [Bergmark, 1989; Crisco and Panjabi, 1991]. In this work, a novel kinematics-based methad [Shirazi-Adl et al., 2002] is first applied to compute muscle forces and internal loads in standing postures under gravity with or without 200N loads held either on sides or close to the body in front. The stability of the system under given loads and prescribed postures is sudsequently examined using both linear bucking analysis based on the deformed configurations and nonlinear analysis while employing a liner stiffness-force relationship for muscules [Bergmark, 1989; Crico and Panjabi, 1991]. The relative accuracy of foregoing methods in stability analysis of some sample structures is also investigated. Moreover, the effect of co-activity on stability of the spine in neutral postures is studied.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".