Bibliographic record
Abstract
En 2001, Sergey Fomin et Andrei Zelevinsky ont introduit un \nprocédé combinatoire appelé mutation modifiant localement un carquois, c'est \nà dire un graphe orienté fi ni. L'application récursive de ce procédé à un carquois \ndonné génère une liste de carquois qui peut être finie ou infinie. Le \nproblème de la classification des carquois donnant une liste fi nie, bien que de \nnature simple, a demandé plusieurs années de travail avant d'être résolu par \nAnna Felikson, Michael Shapiro et Pavel Tumarkin en novembre 2008. \nDans cet article, nous introduisons de manière élémentaire la notion de mutation \net présentons la classification de Felikson, Shapiro et Tumarkin d'un \npoint de vue à la fois mathématique et épistémologique. \nCet article fait suite à un exposé donné au Club Mathématique de l'université \nde Sherbrooke en septembre 2009.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".