NARMAX representation and identification of ankle dynamics
Why this work is in the frame
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Bibliographic record
Abstract
Representation and identification of a parallel pathway description of ankle dynamics as a model of the nonlinear autoregressive, moving average exogenous (NARMAX) class is considered. A nonlinear difference equation describing this ankle model is derived theoretically and shown to be of the NARMAX form. Identification methods for NARMAX models are applied to ankle dynamics and its properties investigated via continuous-time simulations of experimental conditions. Simulation results show that 1) the outputs of the NARMAX model match closely those generated using continuous-time methods and 2) NARMAX identification methods applied to ankle dynamics provide accurate discrete-time parameter estimates. Application of NARMAX identification to experimental human ankle data models with high cross-validation variance accounted for.
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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) | 0.000 | 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