Sensitivity of kinematics-based model predictions to optimization criteria in static lifting tasks
Bibliographic record
Abstract
The effect of eight different cost functions on trunk muscle forces, spinal loads and stability was investigated. Kinematics-based approach combined with nonlinear finite element modeling and optimization were used to model in vivo measurements on isometric forward flexions at approximately 40 degrees and approximately 65 degrees in sagittal plane with or without a load of 180N in hands. Four nonlinear (summation stress(3), summation stress(2), summation force(2) and muscle fatigue) and four linear (summation stress, summation force, axial compression and double-linear) criteria were considered. Predicted muscle activities were compared with measured EMG data. All predictions, irrespective of the cost function used, satisfied required kinetic, kinematics and stability conditions all along the spine. Four criteria (summation stress(3), summation stress(2), fatigue and double-linear) predicted muscle activities that qualitatively matched measured EMG data. The fatigue and double-linear criteria were inadequate in predicting greater forces in larger muscles with no consideration for their moment arms. Nearly the same stability margin was computed under these four cost functions. At the lower lumbar levels, the compression forces differed by <20% and the shear forces by <14% as various cost functions were considered. Smaller axial compression and anterior shear forces (by less than or approximately equal 6%) were computed when only the active components rather than the total muscle forces were taken as unknown in the summation stress(3) cost function. Overall, one single cost function of summation stress(2) or summation stress(3) rather than a multi-criteria one was found sufficient and adequate in yielding plausible results comparable with measured EMG activities and disc pressure.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".