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Record W4293242248 · doi:10.47339/ephj.2021.170

Accuracy of a commercial lead test kit

2021· article· en· W4293242248 on OpenAlexvenueno aff
Nick Park, Environmental Health BCIT School of Health Sciences, Dale Chen, María Guadalupe Guzmán Tirado, Hsin Chuan Kuo

Bibliographic record

VenueBCIT Environmental Public Health Journal · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality and Pollution Assessment
Canadian institutionsnot available
Fundersnot available
KeywordsLead (geology)Test (biology)Health hazardRisk analysis (engineering)HazardResidualLead exposureComputer scienceLead poisoningEnvironmental healthReliability engineeringEnvironmental scienceBusinessEngineeringMedicineTelecommunicationsBiologyEcology

Abstract

fetched live from OpenAlex


 Up until 1960s, lead was widely used for constructing plumbing systems, and a residual amount of lead is still detected within water systems today. Due to the wide availability, low-cost, and ability to produce an instant result, commercial lead test kits have been known for their convenience. However, considering that small lead exposures can pose serious health concerns to those who are vulnerable, inaccurate results may cause a potential health hazard. This study investigated the accuracy of a commercial lead test kit called “10-in-1 Drinking Water Test Kit” by Baldwin Meadows and compare its findings to instrumental analysis.

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 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 score0.438
Threshold uncertainty score0.984

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0170.001

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 head0.069
GPT teacher head0.321
Teacher spread0.252 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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