Bibliographic record
Abstract
Le guide Ulysse Escale a Toronto est l'outil ideal pour tirer le maximum d'un court sejour dans la metropole de l'Ontario et du Canada. Pratique de par son format et l'organisation de son contenu ou l'on trouve l'essentiel en un coup d'?il, le guide de voyage Escale a Toronto vous revelera tous les secrets et les bonnes adresses de TO, ville moderne au rythme trepidant. Escale a Toronto est aussi agreable a consulter grâce a sa mise en page en couleurs et ses nombreuses photos. Le guide de voyage Escale a Toronto est ultra-pratique grâce a sa structure facile a comprendre en un clin d'oeil et a utiliser sur place. La premiere section intitulee « Le meilleur de Toronto» met en lumiere ce que la ville a de mieux a offrir et facilite l'organisation generale de votre escapade a Toronto. La section « Explorer Toronto» propose ensuite 10 itineraires cle en main pour ne rien manquer des differents quartiers de la ville. Pour chaque itineraire, un plan double-page clair et precis indique le trace du circuit, en plus de localiser attraits, cafes, restaurants, bars, boites de nuit, salles de spectacle, boutiques et hotels. Impossible de louper quoi que ce soit! Qui plus est, un systeme d'etoiles et de labels coup de coeur guide le lecteur vers les adresses qui se demarquent. Le chapitre « Toronto pratique », bourre de renseignements utiles livres de maniere succincte et aisement reperable, complete l'ouvrage. A tout cela s'ajoutent des cartes additionnelles - vue generale de la ville, zoom sur le centre-ville, plan du metro.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.275 | 0.080 |
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".