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Record W2080551892 · doi:10.1021/es049335x

Contribution of Municipal Effluents to Metal Fluxes in the St. Lawrence River

2004· article· en· W2080551892 on OpenAlexaffabout
Charles Gobeil, Bernard Rondeau, Luc Beaudin

Bibliographic record

VenueEnvironmental Science & Technology · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy metals in environment
Canadian institutionsInstitut National de la Recherche ScientifiqueUniversité du Québec à Montréal
Fundersnot available
KeywordsEffluentParticulatesEnvironmental chemistryEnvironmental scienceDeposition (geology)MetalTrace elementSurface waterOrganic matterTrace metalHydrology (agriculture)ChemistryEnvironmental engineeringSedimentGeology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.512
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.006
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.008
GPT teacher head0.242
Teacher spread0.234 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations78
Published2004
Admission routes2
Has abstractyes

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