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Record W3081394388 · doi:10.1002/aws2.1182

Potential regulatory implications of Health Canada's new lead guideline

2020· article· en· W3081394388 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueAWWA Water Science · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy Metal Exposure and Toxicity
Canadian institutionsDalhousie University
Fundersnot available
KeywordsGuidelineDaytimeSampling (signal processing)Lead (geology)Environmental scienceEnvironmental healthLead exposureMedicineEngineeringTelecommunicationsAtmospheric sciences

Abstract

fetched live from OpenAlex

Abstract Health Canada's guideline for lead in drinking water was updated in March 2019. Two new sampling protocols were introduced—random daytime and 30‐min stagnation sampling—and the maximum acceptable concentration (MAC) of lead in drinking water was decreased from 10 to 5 μ g/L. This study examined the possible impacts that changes in the guideline might have on water utilities in Canada. A lead‐monitoring survey of seven drinking water distribution systems was conducted using the random daytime and 30‐min stagnation protocols. Random daytime sampling captured an estimated 45% more lead than 30‐min stagnation sampling. However, both protocols yielded samples above the new MAC: 7.5% and 5.4% of random daytime and 30‐min stagnation samples, respectively, exceeded it. These data indicate that some drinking water providers—especially those supplying systems with legacy lead plumbing—may have difficulty achieving 100% compliance with the new guideline.

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)0.001
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 score0.328
Threshold uncertainty score0.952

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.019
GPT teacher head0.247
Teacher spread0.228 · 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