Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This work deals with the assessment of neurological diseases known as dysarthrias, using a novel approach based on objective and perceptual features extracted from pathological speech signals. A methodology for the classification of dysarthria is developed in which digital signal processing algorithms are used to appraise the severity of those features less reliably judged by the clinicians, while the others are taken directly from perceptual judgments or medical records. The assessment process evaluates the performance of two different classifiers and compares them with the traditional assessment system. The first approach is based on the lineal discriminant analysis and the second is a non-lineal technique based on self-organizing maps. The non-lineal classifier provided the highest percent of correct classification and the most accurate information on the relevance of the features in the classifier decision. It also provided a bi-dimensional representation of de data that allows a better understanding of the correspondence between the speech deviations and the location of the damage in the peripheral or central nervous system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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