Physicochemical Quality of Surface Water: Background Study Prior of the Milky River Sub-Basin, Abitibi, Canada
Bibliographic record
Abstract
In spite of its economic benefits, the mining industry can have many negative impacts on the environment, more specifically, on surface water quality. The evaluation of the impact of the mining activity must therefore be addressed adequately with a background study prior to mining operation areas. Thirteen active and past producing mines are located inside the sub-basin of the Milky. These mining sites may contribute to the water mineralization of the surface waters and sediments of the Milky river system. In the present study, nine metals including As, Cd, Cr, Cu, Fe, Mn, Ni, Pb, and Zn were measured at thirty (30) surface water locations during two years in the catchment area of the Milky river. The data collected served to calculate the Metal pollution index (MPI) and the Metal index (MI) to evaluate the surface water quality. The MI values suggest that all the samples are contaminated, while only some of the samples are considered contaminated according to the MPI values. However, it is impossible to discriminate the origin of this contamination between the natural enriched geochemical background of this sub-basin and the anthropic activities. This discrepancy between the two pollution evaluation methods demonstrate that their interpretation needs to be adapted to the context of mining districts that have both a high natural geochemical background and are affected by past and present mining activities.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 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) | 0.001 | 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".