Effects of tendon and plantar cutaneo-muscular vibration on postural control during quiet and perturbed standing
Bibliographic record
Abstract
Entreprendre et compléter un doctorat est un travail de longue haleine, qui comporte son lot de stress et de périodes de découragement.Malgré les moments plus difficiles, j'en retire aujourd'hui l'incroyable fierté d'avoir enfin réussi.Ces années m'ont permis de développer des qualités professionnelles et personnelles dont je ne soupçonnais pas l'existence et surtout, ils ont fait de moi une personne enfin confiante en ses moyens.Cela a été rendu possible en grande partie grâce aux personnes dévouées et passionnées que j'ai croisées dans ma vie professionnelle, ainsi qu'à mes proches qui sont toujours là.I would like to thank my supervisor, Dr. Joyce Fung, for her constant support and understanding.In addition to her great contribution to the quality of this research, she always had a word of encouragement when I needed it the most.I am also grateful for the respect and support she showed when faced with the personal choices I made during my doctoral studies.I would also like to thank my co-supervisor, Dr. Marc Bélanger.Through all these years, I knew I could count on him, any time for any reason.He was always available for a good discussion and he made me feel that time was never an issue when I needed his feedback.He put a lot of trust in me, and this helped me become a more self-confident person.
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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 | 0.002 |
| 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) | 0.007 | 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".