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Record W2474882976 · doi:10.1021/acs.est.6b01912

Evaluating the Effects of Full and Partial Lead Service Line Replacement on Lead Levels in Drinking Water

2016· article· en· W2474882976 on OpenAlexafffund
Benjamin F. Trueman, Eliman Camara, Graham A. Gagnon

Bibliographic record

VenueEnvironmental Science & Technology · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy Metal Exposure and Toxicity
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsLead (geology)Environmental scienceLead exposureService (business)Lead poisoningLead timeEnvironmental engineeringEnvironmental healthRisk analysis (engineering)BusinessEngineeringOperations managementMedicineTelecommunicationsGeology

Abstract

fetched live from OpenAlex

Lead service line replacement (LSLR) is an important strategy for reducing lead exposure via drinking water, but jurisdictional issues can sometimes interfere with full replacement of the lead line. The effects of full and partial LSLR on lead levels were assessed using 5 × 1-L sample profiles collected at more than 100 single-unit residences. Profiles comprised four sequential standing samples (L1-L4) and a free-flowing sample (L5) drawn after a 5 min flush of the outlet. At 45 sites with full lead service lines, 90th percentile lead levels in standing samples ranged from 16.4 to 44.5 μg L(-1) (L1 and L4, respectively). In the free-flowing sample (L5), 90th percentile lead was 9.8 μg L(-1). Within 3 days, full LSLR had reduced L3-L5 lead levels by more than 50%, and within 1 month, lead levels were significantly lower in every liter of the sample profile. Conversely, partial LSLR more than doubled premises plumbing (L1, L2) lead release in the short term and did not reduce L1, L2 lead release in the long term. Even 6 months after partial LSLR, 27% of first-draw lead levels were greater than 15 μg L(-1) (the U.S. EPA action level), compared with 13% pre-replacement.

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 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 score0.061
Threshold uncertainty score0.699

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.023
GPT teacher head0.282
Teacher spread0.259 · 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

Citations58
Published2016
Admission routes2
Has abstractyes

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