La qualité de l'eau du secteur fluvial : la contamination par les toxiques
Bibliographic record
Abstract
Au cours du siecle dernier, l’urbanisation, les activites industrielles et les activites agricoles ont genere une importante charge de substances toxiques qui se sont retrouvees dans les cours d’eau. Ces apports ont contribue a deteriorer la qualite de l’eau de l’immense bassin Grands Lacs–Saint-Laurent, mettant ainsi en peril la sante de cet ecosysteme unique. Les stations de reference du secteur fluvial servent a evaluer l’etat de la contamination de l’eau en enregistrant les fluctuations saisonnieres et interannuelles ainsi que les tendances a long terme des concentrations de contaminants. Depuis 1995, la region de Quebec (figure 1) sert de station de reference, puisqu’elle cumule la contamination des differentes masses d’eau qui composent le fleuve Saint-Laurent et y sont melangees sous l’effet des marees. Depuis 2003, des mesures sont egalement prises a l’ile Wolfe, a la sortie du lac Ontario (figure 1). Cette station de reference permet d’evaluer la qualite de l’eau provenant des Grands Lacs, qui est caracterisee par des eaux claires et mineralisees. En sus de ces deux stations localisees a l’entree et a l’exutoire de la partie fluviale du Saint-Laurent, une nouvelle station s’est ajoutee en 2004, pres de l’embouchure de la riviere des Outaouais, a Carillon. Les eaux de l’Outaouais, le plus important tributaire du fleuve, couvrent une large portion de la rive nord du Saint-Laurent et sont fortement colorees. Ces eaux, appelees communement eaux brunes, peuvent etre identifiees facilement jusqu’a Trois-Rivieres.
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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 | 0.000 | 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".