Algorithmes pour la prise de decision distribuee en contexte hierarchique
Bibliographic record
Abstract
mon grand amour, mes trsors Je tiens d'abord remercier mes directeur et codirecteur de thse, messieurs Pesant et Frayret.Monsieur Pesant a accept avec enthousiasme de diriger cette thse; j'ai bnfici et appris normment de cette collaboration.Quant monsieur Frayret, je n'arrive pas me rappeler l'avoir rellement choisi comme codirecteur : l'poque, je lui ai prsent mon projet et la relation s'est dveloppe naturellement, progressivement.Ses encouragements sans cesse renouvels ont constitu un instrument des plus prcieux.Merci galement madame Sophie D'Amours qui m'a accueilli au Consortium de recherche FORAC, il y a sept ans dj.Elle dispose d'une facult unique : celle de permettre aux gens de s'panouir et de raliser leurs rves, en acceptant d'aller au-del des conventions, hors des sentiers battus.Tout ceci aurait t impossible sans elle.Je lui en suis trs reconnaissant.galement, je ne pourrais passer sous silence la contribution de monsieur Alain Rousseau.Il fut mon mentor -mon matre - mon arrive au consortium.Merci aussi Constance Van Horne -prsidente de mon fan club -qui
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".