Contribution of Municipal Effluents to Metal Fluxes in the St. Lawrence River
Bibliographic record
Abstract
The contribution of urban effluents to the total metal fluxes carried toward the sea by the St. Lawrence, a major world river, is 60% for Ag; 8-13% for Cu, Zn, Mo, Cd, and Bi; and less than 3% for all other measured elements (Al, V, Cr, Mn, Fe Co, Ni, As, Rb, Sr, Zr, Cs, Ba, W, Re, Pb, Th, U). This is inferred from measurements at the Montreal wastewater treatment plant. Except for Ag, municipal effluents do not weigh heavily on the St. Lawrence River metal budget, likely because of the physical-chemical primary treatment applied to most effluents. Compared to direct atmospheric deposition on the surface of the river, effluents would contribute half as much Pb and one-tenth as much Zn. In contrast, effluents deliver twice as much Cd and six times as much Cu as the atmosphere. Stable Pb isotope ratios (206Pb/207Pb, 206Pb/208Pb) in suspended particulate matter from the river indicate that the total Pb content in the river water is three times higher than the pristine level. The ratios of Cr, Ni, Cu, Zn, and Cd to Al in suspended particulate matter are high as compared to pre-industrial sediments, which suggeststhattrace elementfluxes are higher today. To decrease metal levels in the St. Lawrence River further will be a challenge since the sources of metals are not well-known.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".