Cleaning Up after the Log Drivers' Waltz: Finding the Ottawa River Watershed
Bibliographic record
Abstract
The Ottawa River has experienced the environmental impact of a succession of industrial uses from the lumber trade through hydroelectric power development to pulp and paper manufacturing. Each of these activities, and the introduction of substantial discharges of urban wastewater, has been the subject of legal intervention and regulatory controls. The present paper surveys this largely sectoral approach to water quality protection along the Ottawa River and notes in conclusion tentative steps being taken to formulate a more comprehensive and coherent framework on a watershed basis.Qu’il s’agisse du commerce du bois, de l’hydroelectricite ou des manufactures de pâtes et papiers, les activites industrielles menees sur la riviere des Outaouais ont toutes eu des impacts environnementaux. Chacune de ces activites, de meme que le rejet des eaux usees dans la riviere, a fait l’objet de controles juridiques et reglementaires. Dans cet article, l’auteur analyse l’approche sectorielle ayant prevalu en matiere de protection de la qualite de l’eau et il note, en conclusion, certaines tentatives recentes visant a instaurer un cadre global et coherent, qui tiendrait compte des bassins versants. (Article seulement disponible en anglais.)
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".