Rehausser la surveillance communautaire des eaux au Canada
Bibliographic record
Abstract
En novembre 2018, Living Lakes Canada, WWF-Canada, et la Gordon Foundation ont organisé une table ronde nationale visant à identifier les mesures concrètes que le gouvernement fédéral peut prendre pour démontrer son leadership et son soutien dans l’avancement de la surveillance communautaire des écosystèmes d’eau douce au Canada. Cette compilation est le résultat de la table ronde. Elle comprend un document de travail, des recommandations finales et des études de cas de divers programmes de surveillance communautaire à travers le pays. Ce travail a été rendu possible grâce aux idées et à la contribution des participants à la table ronde et du comité consultatif du projet. Cette initiative a été réalisée avec le soutien d’Environnement et changement climatique Canada et de Relations Couronne-Autochtones et Affaires du Nord Canada.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.019 | 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; both teacher heads agree on what is shown here.
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".