Analysis of normal swallowing sounds using nonlinear dynamic metric tools
Bibliographic record
Abstract
Several metric tools for quantative analysis of scalar time series have been developed using the theory of nonlinear dynamics. The goal of this work was to study the characteristics of swallowing sound using these metric tools. Takens method of delays was used to reconstruct multidimensional state space representation of the swallowing sounds of 6 healthy subjects (ages 13-30 years, 3 males) being fed thin and thick liquid textures. The optimum time delay for different subjects varied from 3 to 9 samples. False nearest neighbors method was used to obtain proper embedding dimension. The correlation dimension was calculated based on Grassberger-Procaccia algorithm. The results suggest that swallowing sound is well characterized by a small number of dimensions. The largest Lyapunov exponent was also estimated to evaluate the presence of chaos. As the largest Lyapunov exponent for some cases was negative, it may be concluded that swallowing sound is not necessarily a chaotic process.
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How this classification was reachedexpand
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 | 0.001 |
| 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 teacher head, 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".