La qualité de l'eau du secteur fluvial : paramètres physico-chimiques et bactériologiques
Bibliographic record
Abstract
D’importantes interventions d’assainissement des eaux usees municipales ont ete completees dans les bassins versants du Saint-Laurent ainsi que dans les municipalites riveraines du fleuve au cours des 20 dernieres annees. Des actions ont egalement ete menees le long du corridor fluvial et dans les basses-terres du Saint-Laurent pour diminuer la pollution d’origine agricole. Le programme de suivi de la qualite de l’eau a l’aide de parametres lies a la pollution non toxique (eutrophisation, hypoxie, erosion, contamination fecale et organique) permet de mesurer les retombees environnementales de ces interventions et de celles qui seront realisees dans le futur. Ce programme permet egalement de mettre en evidence les impacts de la modification du regime d’ecoulement sur la qualite de l’eau. Le reseau de surveillance exploite par le ministere du Developpement durable, de l’Environnement et des Parcs du Quebec est compose de 31 stations d’echantillonnage et s’etend de l’exutoire du lac Saint-Francois jusqu’a la pointe ouest de l’ile d’Orleans. Enfin, un suivi a haute frequence est realise a la hauteur de Quebec (prise d’eau de Levis) par Environnement 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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".