Mechanisms underlying a third-order parametric model of dynamic reflex stiffness
Bibliographic record
Abstract
A parallel-cascade system identification method was used to identify the reflex contribution to dynamic ankle stiffness in both normal and spastic spinal cord injured (SCI) subjects. Reflex dynamics were estimated by the impulse response function between half-wave rectified velocity and reflex torque. Parametric models were fitted to these IRFs using non-linear, least-squares methods. A simple, second-order low-pass model described reflex dynamic stiffness well for normal subjects at low contraction levels. However, this model was inadequate for normal subjects, at high contraction levels, and for SCI subjects at all contraction levels. Good fits were obtained using a third-order model consisting of a second-order low-pass in series with a first order pole. We hypothesized that the third order model might arise from a "clonus-like" reflex activation that comprised several distinct bursts of activity. Simulation studies showed that this pattern of reflex activation in series with muscle dynamics gave an overall response which was described very well by a third order model. Sensitivity studies demonstrated the systematic dependence of the parameters of the third-order model on the interval between bursts of activation and their decay.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 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".