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Record W2785078796 · doi:10.1021/acssensors.8b00013

An Exciting Year Ahead for <i>ACS Sensors</i>

2018· editorial· en· W2785078796 on OpenAlexaffabout
J. Justin Gooding, Antonella I. Mazur, Michael J. Sailor, Maarten Merkx, Shana O. Kelley, Nongjian Tao, Yi‐Tao Long, Eric Bakker

Bibliographic record

VenueACS Sensors · 2018
Typeeditorial
Languageen
FieldComputer Science
TopicSensor Technology and Measurement Systems
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsNanotechnologyComputer scienceMedicineMaterials science

Abstract

fetched live from OpenAlex

ADVERTISEMENT RETURN TO ISSUEEditorialNEXTAn Exciting Year Ahead for ACS SensorsJ. Justin Gooding, Antonella Mazur, Michael Sailor, Maarten Merkx, Shana Kelley, Nongjian Tao, Yitao Long, and Eric BakkerView Author Information The University of New South Wales, Sydney, Australia ACS Publications, Washington, DC, United States University of California, San Diego, California United States Technische Universiteit Eindhoven, Eindhoven, The Netherlands The University of Toronto, Toronto, Ontario Canada Arizona State University, Tempe, Arizona, United States East China University of Science and Technology, Shanghai, China The University of Geneva, Geneva, SwitzerlandCite this: ACS Sens. 2018, 3, 1, 1–2Publication Date (Web):January 26, 2018Publication History Received5 January 2018Published online26 January 2018Published inissue 26 January 2018https://pubs.acs.org/doi/10.1021/acssensors.8b00013https://doi.org/10.1021/acssensors.8b00013editorialACS PublicationsCopyright © 2018 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 Views1173Altmetric-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 InRedditEmail PDF (696 KB) Get e-AlertscloseSUBJECTS:Sensors 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)0.002
metaresearch head score (Gemma)0.003
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: Editorial · Consensus signal: none
Teacher disagreement score0.610
Threshold uncertainty score0.557

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0020.001
Scholarly communication0.0060.005
Open science0.0020.003
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.6100.530

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 head0.023
GPT teacher head0.276
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 source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
Domainnot available
GenreEditorial

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".

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Citations0
Published2018
Admission routes2
Has abstractyes

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