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Record W3023017908 · doi:10.1002/awwa.1496

Lead in Drinking Water: A Canadian Perspective

2020· article· en· W3023017908 on OpenAlexaboutno aff
Klas Ohman, Steve Craik, Larry M. C. Chow

Bibliographic record

VenueAmerican Water Works Association · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Treatment and Disinfection
Canadian institutionsnot available
Fundersnot available
KeywordsLead (geology)LegislationFlexibility (engineering)BusinessService (business)GuidelineRisk analysis (engineering)Perspective (graphical)Environmental planningEnvironmental healthLead exposureWater infrastructureEnvironmental resource managementWater supplyEngineeringMedicineMarketingPolitical scienceComputer scienceEnvironmental scienceEconomicsEnvironmental engineeringTelecommunications

Abstract

fetched live from OpenAlex

Key Takeaways Lead in drinking water is a top public health issue in the United States and Canada, with similarities and differences in how each country addresses it. Although lacking specifics in some aspects, Canada's new legislation for lead levels in drinking water means more stringent rules and more comprehensive methods for testing and treating. Strategies for sampling and testing are less prescriptive with this new guideline, allowing more flexibility in setting up a lead monitoring and service replacement program. The strategies used to replace and pay for lead service lines differ among various Canadian water utilities.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.194
Threshold uncertainty score0.999

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)0.0000.001

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 head0.006
GPT teacher head0.197
Teacher spread0.192 · 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 designObservational
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

Citations4
Published2020
Admission routes1
Has abstractyes

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