IMF-based chaotic characterization of AP and ML visually-driven postural responses
Bibliographic record
Abstract
The objective was to analyze visually driven postural responses and characterize any non-linear behaviour. We recorded physiological responses for two adults, 260 trials each. The subjects maintained quite stance while fixating for four seconds within an immersive room, EON Icube, where the reference to the visual stimuli, i.e., the virtual platform, randomly oscillated in Gaussian orientation 90° and 270° for antero-posterior (AP), and, 0° and 180° for medio-lateral (ML) at three different frequencies (0.125, 0.25, and 0.5 Hz). We accomplished stationary derivatives of posture time series by taking the intrinsic mode functions (IMFs). The phase space plot of IMF shows evidence of the existence of non-linear attractors in both ML and AP. Correlation integral slope with increasing embedding dimension is similar to random white noise for ML, and similar to non-linear chaotic series for AP. Next, recurrence plots indicate the existence of more non-linearity for AP than that for ML. The patterns of the dots after 200th time stamp (near onset) appears to be aperodic in AP. At higher temporal windows, AP entropy tends more toward chaotic series, than that of ML. There are stronger non-linear components in AP than that in ML regardless of the speed conditions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".