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Record W4283704884 · doi:10.1021/acsestwater.2c00236

Sampling Microplastics in Water Matrices: A Need for Standardization

2022· article· en· W4283704884 on OpenAlexafffundabout
Husein Almuhtaram, Robert C. Andrews

Bibliographic record

VenueACS ES&T Water · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicMicroplastics and Plastic Pollution
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCitationSocial mediaStandardizationAltmetricsLibrary scienceComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

ADVERTISEMENT RETURN TO ISSUEViewpointNEXTSampling Microplastics in Water Matrices: A Need for StandardizationHusein Almuhtaram*Husein AlmuhtaramDepartment of Civil and Mineral Engineering, University of Toronto, Toronto, ON M5S 1A4, Canada*Email: [email protected]More by Husein Almuhtaramhttps://orcid.org/0000-0003-2315-9543 and Robert C. AndrewsRobert C. AndrewsDepartment of Civil and Mineral Engineering, University of Toronto, Toronto, ON M5S 1A4, CanadaMore by Robert C. AndrewsCite this: ACS EST Water 2022, 2, 8, 1276–1278Publication Date (Web):June 29, 2022Publication History Published online29 June 2022Published inissue 12 August 2022https://pubs.acs.org/doi/10.1021/acsestwater.2c00236https://doi.org/10.1021/acsestwater.2c00236article-commentaryACS PublicationsCopyright © 2022 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 Views2614Altmetric-Citations1LEARN 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 (819 KB) Get e-AlertscloseSUBJECTS:Computer simulations,Contamination,Drinking water,Filtration,Peptides and proteins 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)0.261
metaresearch head score (Gemma)0.196
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.261
Threshold uncertainty score0.912

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2610.196
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0040.002
Bibliometrics0.0070.007
Science and technology studies0.0040.018
Scholarly communication0.0150.021
Open science0.0090.010
Research integrity0.0070.020
Insufficient payload (model declined to judge)0.0070.010

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 head0.012
GPT teacher head0.217
Teacher spread0.205 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations25
Published2022
Admission routes3
Has abstractyes

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