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Record W4414525487 · doi:10.1002/aws2.70039

Comparison of Real Versus Synthetic <scp>NOM</scp> on Lead and Copper Release Using Dump and Fill Studies

2025· article· en· W4414525487 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueAWWA Water Science · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicMercury impact and mitigation studies
Canadian institutionsDalhousie UniversityUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCopperLead (geology)Raw waterGalvanic cellWater treatmentDissolved organic carbonSurface waterColloid

Abstract

fetched live from OpenAlex

ABSTRACT The main objective of this study was to evaluate and compare the impact of real and synthetic NOM on lead and copper release from galvanic corrosion. A 21‐week “dump and fill” experiment was completed using test pieces with new lead and copper pipes exposed to various drinking waters. The real waters consisted of unchlorinated, but otherwise conventionally treated, river water and raw municipal well water. Each real water was simulated using two synthetic waters: one with Suwannee River NOM (SRNOM) at the same DOC concentration as in the real water and another without NOM. The synthetic waters with SRNOM released the most dissolved lead, followed by the real waters, and finally by the synthetic waters without SRNOM. Using advanced techniques of characterizing colloidal lead and NOM, complexation was found to be responsible for much of the NOM‐induced dissolved lead release, and humic substances were the component that complexed most strongly.

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.

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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.285
Threshold uncertainty score0.689

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
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 head0.070
GPT teacher head0.361
Teacher spread0.291 · 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