Protocol for Quantifying the Doping of Organic Hole-Transport Materials
Bibliographic record
Abstract
ADVERTISEMENT RETURN TO ISSUEPREVViewpointNEXTProtocol for Quantifying the Doping of Organic Hole-Transport MaterialsRebecka L. ForwardRebecka L. ForwardDepartment of Chemistry, The University of British Columbia, 2036 Main Mall, Vancouver, British Columbia, Canada V6T 1Z1More by Rebecka L. Forward, Kitty Y. ChenKitty Y. ChenDepartment of Chemistry, The University of British Columbia, 2036 Main Mall, Vancouver, British Columbia, Canada V6T 1Z1More by Kitty Y. Chen, David M. WeekesDavid M. WeekesDepartment of Chemistry, The University of British Columbia, 2036 Main Mall, Vancouver, British Columbia, Canada V6T 1Z1More by David M. Weekes, David J. DvorakDavid J. DvorakStewart Blusson Quantum Matter Institute, The University of British Columbia, 2355 East Mall, Vancouver, British Columbia, Canada V6T 1Z4More by David J. Dvorakhttp://orcid.org/0000-0001-5767-3693, Yang CaoYang CaoDepartment of Chemistry, The University of British Columbia, 2036 Main Mall, Vancouver, British Columbia, Canada V6T 1Z1Stewart Blusson Quantum Matter Institute, The University of British Columbia, 2355 East Mall, Vancouver, British Columbia, Canada V6T 1Z4More by Yang Caohttp://orcid.org/0000-0001-8636-7919, and Curtis P. Berlinguette*Curtis P. BerlinguetteDepartment of Chemistry, The University of British Columbia, 2036 Main Mall, Vancouver, British Columbia, Canada V6T 1Z1Department of Chemical and Biological Engineering, The University of British Columbia, 2360 East Mall, Vancouver, British Columbia, Canada V6T 1Z3Stewart Blusson Quantum Matter Institute, The University of British Columbia, 2355 East Mall, Vancouver, British Columbia, Canada V6T 1Z4Canadian Institute for Advanced Research (CIFAR), 661 University Avenue, Toronto, Ontario, Canada M5G 1M1*E-mail: [email protected]More by Curtis P. Berlinguettehttp://orcid.org/0000-0001-6875-849XCite this: ACS Energy Lett. 2019, 4, 10, 2547–2551Publication Date (Web):September 30, 2019Publication History Received13 August 2019Accepted6 September 2019Published online30 September 2019Published inissue 11 October 2019https://pubs.acs.org/doi/10.1021/acsenergylett.9b01766https://doi.org/10.1021/acsenergylett.9b01766article-commentaryACS PublicationsCopyright © 2019 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 Views3234Altmetric-Citations27LEARN 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 (928 KB) Get e-AlertscloseSupporting Info (1)»Supporting Information Supporting Information SUBJECTS:Absorption,Calibration,Doping,Oxidation,Structure activity relationship Get e-Alerts
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.190 | 0.089 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".