Single trial versus ensemble data methods for identification of time-varying elbow joint dynamics
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Bibliographic record
Abstract
This paper presents the highpass mean removal ensemble data (HMRED) method, for identification of human joint dynamics. This method is shown to be more robust to inter-trial variation than previous ensemble mean removal ensemble data (EMRED) methods. The HMRED method is also compared to the single-trial exponentially weighted least squares (EWLS) method. Under similar conditions, EWLS can track parameter variations up to 1 Hz, the HMRED method up to 5 Hz. The HMRED and EWLS methods were verified against each other by applying them to the same experimental data. Introduction Although EWLS [4] requires only a single trial, its tracking capability is limited to low frequencies. Ensemble data methods can track faster variations of a system dynamics provided that the trials are lined up properly [2]. We have found that previous EMRED methods are not very robust to inter-trial variation, because the mean movement is calculated from ensemble data. In this paper we present a robust ensemble ...
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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 | 0.001 | 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