Bibliographic record
Abstract
Medaille aux Jeux Olympiques de Barcelone (1992) et de Sydney (2000), medaille a trois championnats du Monde, aux jeux du Commonwealth, ceux de la Francophonie et les Panamericains, Nicolas Gill est considere par la critique et le public comme le plus grand judoka du Canada. Detenteur d’un 7e dan, l’ancien eleve de Me Hiroshi Nakamura au dojo Notre- Dame-de-Grâce a impressionne le public et les specialistes par ses nombreuses performances mais tout autant par sa philosophie de combat et ses prises de positions sur son sport de competition qu’il pratique depuis l’âge de six ans. Aujourd’hui, apres sa conquete, celui qu’on a surnomme « l’homme aux mille mouvements », administre desormais les destinees de Judo Canada. Gill a eu un parcours athletique hors du commun : il a su tout au long de sa longue carriere faire le meilleur avec des ressources financieres modestes a l’extreme. Intronise au temple de la renommee du Judo quebecois en 2007, il confiera : « Ce qui me rend heureux, c’est d’avoir fait bonne figure pendant aussi longtemps et d’avoir su rebondir apres les blessures et les changements de categories ». Par-dela son bon esprit sportif reconnu, on retrouve la vaillance, l’humilite et le courage de ceux qu’on appelait autrefois les samourais !
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".