Bibliographic record
Abstract
Armel Mercier est professeur retraite de l’Universite du Quebec a Chicoutimi. Detenteur d’un doctorat en mathematiques de l’Universite Laval, ses recherches se sont concentrees principalement sur le comportement asymptotique de fonctions arithmetiques. Quelques resultats sur les identites de l’analyse combinatoire furent aussi publies. Il est auteur de nombreux livres de mathematiques. Le present ouvrage est une introduction aux equations differentielles, a l’analyse de Fourier et au calcul operationnel. Plus precisement, dans le present texte, une attention toute particuliere a ete consacree a l’etude des series de Fourier et a la transformee de Laplace. Cet ouvrage s’adresse donc aux etudiant(e)s du premier cycle qui sont interesse(e)s a connaˆitre et a utiliser certains outils mathematiques que l’on rencontre dans le domaine des mathematiques appliquees. Pour que ce texte soit offert a un vaste auditoire, nous n’avons pas justifie les nouveaux concepts obtenus par celui de la convergence uniforme des series de fonctions ainsi que celui de la convergence uniforme des integrales doubles impropres. Les lecteurs familiers avec ces theories se rendront compte des expressions que nous utiliserons pour justifier les calculs qui nous conduiront a ces nouveaux resultats!
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".