Les travaux de Pascal Nicolas. Interrogation, induction et déduction automatiques pour le raisonnement non monotone... et plus encore
Bibliographic record
Abstract
This article traces all research of Pascal Nicolas. Professor at LERIA, Pascal led the theme Automated reasoning systems for imperfect infortation (Systemes de Raisonnement Automatique pour Informations Imparfaites). He was in particular a recognizes expert of nonmonotonic reasoning and of ASP. Pascal had explored logical formalisms which allow representation of incomplete knowledge, but also uncertain or nonmonotonic, and the characterization of different types of reasoning with these formalisms. Constnt to a scientific approach that he has respected throughout his career, Pascal tackled each of the problems he studied in a global approach ranging from the definition of formal systems to computer implementation.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.014 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".