Selective Electrical Stimulation of Peripheral Nerve Fibers: Accommodation Based Methods
Bibliographic record
Abstract
project was motivated by the idea of using and adapting accommodation-based methods for selective electrical stimulation of motor fibers to the study of the human nociceptive system. This has not been without difficulties, but it has still been a rewarding process, as it has provided the opportunity to study interesting biophysical mechanisms and to enhance the understanding of accommodation based methods. Throughout this project, I am indebted to all the co-workers and friends at the Center of Sensory Motor Interaction and at the Faculty of Dentistry, University of Toronto that I have had the fortune to work with and learn from. I wish to express my sincerest gratitude to my supervisor Associate Prof. Ole K. Andersen and Professor Lars Arendt-Nielsen, the head of Center for Sensory Motor Interaction, for their never failing interest and enthusiasm. I will also like to express my deepest gratitude to my hosts at the Faculty of Dentistry, University of Toronto, Professor Barry J. Sessle and Professor James W. Hu. Furthermore, I will like to thank Dr. Alexandra Vuckovic for graciously offering her volume conductor
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".