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Record W2406572215 · doi:10.1021/acs.est.6b02238

The Flint Water Crisis Confirms That U.S. Drinking Water Needs Improved Risk Management

2016· article· en· W2406572215 on OpenAlexaffabout
Rachel Baum, Jamie Bartram, Steve E. Hrudey

Bibliographic record

VenueEnvironmental Science & Technology · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicWater Quality and Resources Studies
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsRisk managementCrisis managementBusinessWater scarcityEnvironmental scienceWater resource managementWater resourcesEconomicsBiologyFinanceManagement

Abstract

fetched live from OpenAlex

ADVERTISEMENT RETURN TO ISSUEPREVViewpointNEXTThe Flint Water Crisis Confirms That U.S. Drinking Water Needs Improved Risk ManagementRachel Baum*†, Jamie Bartram†, and Steve Hrudey‡View Author Information† The Water Institute, Department of Environmental Sciences and Engineering, Gillings School of Global Public Health, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, United States‡ Analytical & Environmental Toxicology, University of Alberta, Edmonton, Alberta T6G 2G3, Canada*E-mail: [email protected]Cite this: Environ. Sci. Technol. 2016, 50, 11, 5436–5437Publication Date (Web):May 17, 2016Publication History Received5 May 2016Published online17 May 2016Published inissue 7 June 2016https://pubs.acs.org/doi/10.1021/acs.est.6b02238https://doi.org/10.1021/acs.est.6b02238newsACS PublicationsCopyright © 2016 American Chemical Society. This publication is available under these Terms of Use. Request reuse permissions This publication is free to access through this site. Learn MoreArticle Views12459Altmetric-Citations26LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail PDF (620 KB) Get e-AlertscloseSUBJECTS:Drinking water,Impurities,Quality management,Safety,Water treatment Get e-Alerts

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.353
Threshold uncertainty score0.923

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.015
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.003
Science and technology studies0.0020.001
Scholarly communication0.0050.004
Open science0.0010.003
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.3530.124

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 head0.011
GPT teacher head0.196
Teacher spread0.185 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
Domainnot available
GenreCommentary

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

Citations35
Published2016
Admission routes2
Has abstractyes

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