Cadre méthodologique d'identification de stratégies préférentielles de bioraffinage forestier par une approche stage-and-gate
Bibliographic record
Abstract
DÉDICACE À mes parents, Georges et Justine, à qui je dois tout, en particulier mon inspiration et ma force.À mes frères, Bryan, David et Samuel, qui me procurent une perpétuelle envie de me dépasser.REMERCIEMENTS J'aimerais exprimer toute ma gratitude et ma reconnaissance à tous ceux qui de près ou de loin ont supporté mon projet de doctorat, et ont contribué à l'enrichissement intellectuel et personnel qu'il constitue pour moi.Mes premiers remerciements s'adressent au Professeur Paul Stuart, mon directeur de recherche, qui m'a offert l'opportunité de concrétiser un rêve d'enfance, et qui a surtout fait preuve d'une grande confiance à mon égard.Merci d'être un excellent mentor, et de m'avoir prodigué tant de conseils, autant académiques, professionnels que personnels, au fil des années.Je remercie également le Professeur Sophie D'Amours, ma co-directrice de recherche, pour le support qu'elle m'a procuré, et pour les remises en question qu'elle m'a inspirées.Ma gratitude va au Conseil de Recherches en Sciences Naturelles et en Génie du Canada (CRSNG) et au Réseau VCO pour leur soutien financier qui aura été essentiel à la réalisation de ces travaux.I would like to thank the industrial partner companies for supporting the realization of my research, and for their financial as well as in-kind contributions to my project.I would like to express my appreciation to representatives of the industrial partner companies that participated in MCDM panels, especially Rod A., Eddie P., Jerrett D., and Tom B. Your precious time and feedback have been greatly appreciated.I have an infinite gratitude for David P., who supported and supervised me during my internship at the case study mill.I will never forget your hospitality and kindness ; thank you for four beautiful months in the Rockies.I would also like to thank Mike R., Ray M., and Stephen W., whose
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".