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Influence of drinking water quality on the formation of corrosion scales in lead-bearing drinking water distribution systems

2021· article· en· W6958761994 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2021
Typearticle
Languageen
FieldComputer Science
TopicSpeech Recognition and Synthesis
Canadian institutionsnot available
Fundersnot available
KeywordsCorrosionWater qualityLead (geology)Water supplyWater treatmentSoft waterErosion corrosion of copper water tubesGroundwater

Abstract

fetched live from OpenAlex

Lead in drinking water occurs in drinking water distribution systems (DWDS) where lead pipes are used as service lines. Using data gathered from 4 different Canadian cities, we link drinking water quality to composition of corrosion scales obtained on exhumed lead pipes servicing those municipalities. The data presented encompasses a 10-year span and a detailed layer profile analysis of the solids present in lead bearing service lines; where different layers within the corrosion scale formed inside lead pipes are identified and thoroughly characterized. The results obtained clearly show that the corrosion layers in direct contact with drinking water are rich in lead oxides phases and aluminosilicates. In contrast, lead carbonates are the main phases present on corrosion scales in direct contact with the metallic lead pipe. This heterogeneity on phase distribution is correlated to the radial distance from the corrosion scales to the water/solid interphase and water quality servicing those municipalities. Statistical analysis suggests that dissolved Al, Mn, Cu, Ni, and As accumulate on the corrosion scales with preferential accumulation of specific elements heavily dependent on distinct municipality water quality.

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)0.000
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 score0.061
Threshold uncertainty score0.500

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 studies0.0000.000
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 head0.060
GPT teacher head0.269
Teacher spread0.209 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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