Bibliographic record
Abstract
L’industrie minière connaît une effervescence depuis deux décennies. Le développement des technologies de pointe et la croissance soutenue de la Chine stimulent la demande. Le Québec possède de nombreuses ressources minières (fer, nickel, cuivre, zinc, or, etc.). Notre étude vise à dresser un portrait synthétique du Québec minier. Nous tenterons de répondre à différentes questions : Quelle est l’importance du secteur minier au Québec et sa position à l’intérieur de l’ensemble canadien ? Quelles sont les stratégies déployées pour favoriser le développement de l’industrie et atténuer ses impacts ? Comment l’exploitation minière affecte-t-elle les collectivités locales et la qualité de l’environnement ? Nos descriptions et analyses seront alimentées par des données documentaires, des expériences de terrain et des traitements statistiques et cartographiques.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".