Sébastien BUBECK JEUX DE BANDITS ET FONDATIONS DU CLUSTERING Rapporteurs: M. Olivier CATONI CNRS et ENS
Bibliographic record
Abstract
ton enthousiasme permanent, ta disponibilité et ta vision mathématique unique me resteront longtemps en mémoire. Jean-Yves, nous avons tout juste commencé à explorer nos centres d’intérêt communs, et j’ai le sentiment qu’il nous reste encore beaucoup de choses à faire. Je tenais particulièrement à te remercier de partager tes idées toujours très stimulantes (pour ne pas dire plus...) avec moi. Les us et coutumes du monde académique peuvent parfois être difficile à pénétrer, heureusement dans ce domaine j’ai eu comme maitre un expert en la matière, Gilles. Au niveau mathématique tu m’as permis de débuter ma thèse sur des bases solides, et ton aide a été inestimable. I was lucky enough to be introduced to the world of research by you Ulrike. You taught me how to do (hopefully) useful theoretical research, but also all the basic tricks that a researcher has to know. I wish both of us had more time to continue our exciting projects, but I am confident that in the near future we will collaborate again! In the cold and icy land of Alberta, lived a man known for his perfect knowledge of the right references, but also for his constant kindness. Csaba, I am looking forward to (finally) start a new project with you.
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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 | 0.003 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.024 | 0.016 |
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".