Contribution of Municipal Effluents\nto Metal Fluxes in the St. Lawrence\nRiver
Bibliographic record
Abstract
The contribution of urban effluents to the total metal\nfluxes carried toward the sea by the St. Lawrence, a major\nworld river, is 60% for Ag; 8−13% for Cu, Zn, Mo, Cd,\nand Bi; and less than 3% for all other measured elements\n(Al, V, Cr, Mn, Fe Co, Ni, As, Rb, Sr, Zr, Cs, Ba, W, Re,\nPb, Th, U). This is inferred from measurements at the Montreal\nwastewater treatment plant. Except for Ag, municipal\neffluents do not weigh heavily on the St. Lawrence River\nmetal budget, likely because of the physical−chemical primary\ntreatment applied to most effluents. Compared to direct\natmospheric deposition on the surface of the river, effluents\nwould contribute half as much Pb and one-tenth as\nmuch Zn. In contrast, effluents deliver twice as much Cd\nand six times as much Cu as the atmosphere. Stable Pb\nisotope ratios (<sup>206</sup>Pb/<sup>207</sup>Pb, <sup>206</sup>Pb/<sup>208</sup>Pb) in suspended\nparticulate matter from the river indicate that the total Pb\ncontent in the river water is three times higher than the\npristine level. The ratios of Cr, Ni, Cu, Zn, and Cd to Al in\nsuspended particulate matter are high as compared to pre-industrial sediments, which suggests that trace element fluxes\nare higher today. To decrease metal levels in the St.\nLawrence River further will be a challenge since the sources\nof metals are not well-known.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.042 | 0.001 |
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 teacher head, 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".