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Record W2587993072 · doi:10.2166/wqrjc.2017.028

Effect of total organic carbon and aquatic humic substances on the occurrence of lead at the tap

2015· article· en· W2587993072 on OpenAlexaffabout
Lisa D. Winning, Beata Gorczyca, Kenneth Brezinski

Bibliographic record

VenueWater Quality Research Journal · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Treatment and Disinfection
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsChemistryChlorineEnvironmental chemistryTotal organic carbonNatural organic matterOrganic matterDissolved organic carbonCarbon fibersLead (geology)Water treatmentSuspended solidsEnvironmental engineeringEnvironmental scienceWastewaterOrganic chemistry

Abstract

fetched live from OpenAlex

Homes with lead service lines (LSLs) in the City of Brandon, Manitoba, Canada, were found to exceed the provincial standard of 10 μg/L for lead in drinking water. Solids identified by X-ray diffraction of LSL scale were Pb5O8 and PbO2, indicating that lead(II) solids in the LSL scale have been oxidized to lead(IV) solids by free chlorine residuals. Natural organic matter (NOM) can reduce PbO2 within a few hours, and Brandon treated water has high levels of NOM at approximately 5–7.6 mg/L as total organic carbon (TOC). As water stagnates in the LSL during periods of no water use the free chlorine residual is depleted, permitting PbO2 to oxidize NOM and be reduced to more soluble lead(II) species, resulting in an increase in dissolved lead concentrations. Although it is generally believed that aquatic humic substances (AHS) are primarily responsible for the reductant capacity of NOM, removal of AHS from the treated water resulted in a 6% decrease in lead release from PbO2, while removal of 50% of total NOM resulted in a 75% decrease in lead release. AHS and TOC were not found to play a significant role in the reduction of PbO2 in this water.

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)0.007
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.624
Threshold uncertainty score0.243

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.099
GPT teacher head0.372
Teacher spread0.273 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations11
Published2015
Admission routes2
Has abstractyes

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