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Record W2978002064 · doi:10.1021/acs.jafc.9b05074

55th North American Chemical Residue Workshop

2019· article· en· W2978002064 on OpenAlexaffabout
Marc E. Engel, Robert D. Trengove, Teri Besse, Jon W. Wong, Paul Yang, Alex Krynitsky

Bibliographic record

VenueJournal of Agricultural and Food Chemistry · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicPesticide and Herbicide Environmental Studies
Canadian institutionsMinistry of Environment
Fundersnot available
KeywordsLibrary scienceChristian ministryChinaEngineeringArchaeologyGeographyPolitical scienceLawComputer science

Abstract

fetched live from OpenAlex

ADVERTISEMENT RETURN TO ISSUESymposium Introducti...Symposium IntroductionNEXT55th North American Chemical Residue WorkshopMarc E. EngelMarc E. EngelNorth America Chemical Residue Workshop (NACRW), 2910 Kerry Forest Parkway, Tallahassee, Florida 32309, United StatesMore by Marc E. Engel, Robert TrengoveRobert TrengoveSeparation Science and Metabolomics Laboratory and Metabolomics Australia, Western Australia Node, Murdoch University, 90 South Street, Murdoch, Western Australia 6150, AustraliaMore by Robert Trengove, Teri BesseTeri BesseNorth America Chemical Residue Workshop (NACRW), 2910 Kerry Forest Parkway, Tallahassee, Florida 32309, United StatesMore by Teri Besse, Jon WongJon WongCenter for Food Safety and Applied Nutrition, United States Food and Drug Administration, 5001 Campus Drive, College Park, Maryland 20740-3835, United StatesMore by Jon Wonghttp://orcid.org/0000-0003-0116-2880, Paul Yang*Paul YangLaboratory Services Branch, Ontario Ministry of Environment, Conservation and Parks, 125 Resources Road, Etobicoke, Ontario M9P 3V6, Canada*Telephone: 416-235-6004. E-mail: [email protected]More by Paul Yanghttp://orcid.org/0000-0001-6650-9609, and Alex KrynitskyAlex KrynitskySymbiotic Research LLC, International Trade Center, Area 2, 350 Clark Drive, Mount Olive, New Jersey 07828, United StatesMore by Alex KrynitskyCite this: J. Agric. Food Chem. 2019, 67, 46, 12611–12612Publication Date (Web):October 4, 2019Publication History Published online4 October 2019Published inissue 20 November 2019https://doi.org/10.1021/acs.jafc.9b05074Copyright © 2019 American Chemical SocietyRIGHTS & PERMISSIONSArticle Views154Altmetric-Citations-LEARN 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 InReddit Read OnlinePDF (252 KB) Get e-AlertsSUBJECTS:Chromatography,Extraction,Food,Mass spectrometry,Pest control 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)0.001
metaresearch head score (Gemma)0.002
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: Other · Consensus signal: Other
Teacher disagreement score0.708
Threshold uncertainty score0.417

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0040.002
Open science0.0020.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.7080.620

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 head0.005
GPT teacher head0.183
Teacher spread0.177 · 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
GenreOther

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
Published2019
Admission routes2
Has abstractyes

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