Cartes topographiques : les éléments de base
Bibliographic record
Abstract
Les cartes topographiques établies par Ressources naturelles Canada (RNCan) offrent des renseignements détaillés sur un secteur donné et sont utilisées dans plusieurs contextes, notamment la préparation aux situations d'urgence, l'aménagement urbain, l'exploitation des ressources et l'arpentage, ainsi que pour des activités comme le camping, le canotage, les raids sportifs, la chasse et la pêche. Ce guide est conçu pour aider les utilisateurs à comprendre les éléments de base d'une carte topographique. Il offre un aperçu des concepts relatifs à la cartographie et contient des conseils sur l'utilisation des cartes topographiques, des explications sur les termes techniques de même que des exemples de symboles utilisés pour représenter les caractéristiques topographiques sur les cartes.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.020 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.007 |
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".