Coupled objective function to study the role of abdominal muscle forces in lifting using the kinematics-driven model
Bibliographic record
Abstract
To circumvent the existing shortcoming of optimisation algorithms in trunk biomechanical models, both agonist and antagonist trunk muscle stresses to different powers are introduced in a novel objective function to evaluate the role of abdominal muscles in trunk stability and spine compression. This coupled objective function is introduced in our kinematics-driven finite element model to estimate muscle forces and to identify the role of abdominal muscles in upright standing while lifting symmetrically a weight at different heights. Predictive equations for the compression and buckling forces are developed. Results are also compared with the conventional objective function that neglects abdominal muscle forces. An overall optimal solution involving smaller spinal compression but higher trunk stability is automatically attained when choosing muscle stress powers at and around 3. Results highlight the internal oblique muscle as the most efficient abdominal muscle during the tasks investigated. The estimated relative forces in abdominal muscles are found to be in good agreement with the respective ratios of recorded electromyography activities.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".