Strict 2-Surface Proximal Classification of Knee-joint Vibroarthrographic Signals
Bibliographic record
Abstract
Externally detected vibroarthrographic (VAG) signals contain information that can be used to characterize certain pathological aspects of the knee joint. To classify VAG signals as normal or abnormal, we propose to apply both the linear and nonlinear strict 2-surface proximal (S2SP) classifiers based on statistical parameters derived from VAG signals and selected by using a genetic algorithm (GA). A database of VAG signals of 89 human knee joints (51 normal and 38 abnormal) was studied. The classification performance of the linear S2SP classifier reached 0.82 in terms of the area under the receiver operating characteristics curve (Az) and 74.2% in average classification accuracy with the leave-one-out (LOO) procedure. The classification performance of the nonlinear S2SP classifier reached 0.95 in Az value and 91.0% in average classification accuracy using the Gaussian kernel with the LOO procedure, and possessed good robustness around the selected kernel parameter.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".