Resilient Women and the Resiliency of Science
Bibliographic record
Abstract
ADVERTISEMENT RETURN TO ISSUEEditorialNEXTResilient Women and the Resiliency of ScienceNiveen M. Khashab*Niveen M. KhashabSmart Hybrid Materials (SHMs) Laboratory, Advanced Membranes and Porous Materials Center, King Abdullah University of Science and Technology (KAUST), Thuwal 23955-6900, Kingdom of Saudi Arabia*Email: [email protected]More by Niveen M. Khashabhttps://orcid.org/0000-0003-2728-0666, Sara E. Skrabalak*Sara E. SkrabalakDepartment of Chemistry, Indiana University—Bloomington, 800 E. Kirkwood Avenue, Bloomington, Indiana 47405, United States*Email: [email protected]More by Sara E. Skrabalakhttps://orcid.org/0000-0002-1873-100X, Lihi Adler-AbramovichLihi Adler-AbramovichDepartment of Oral Biology, The Goldshleger School of Dental Medicine, Sackler, Faculty of Medicine, and The Center for Nanoscience and Nanotechnology, Tel Aviv University, Tel Aviv 6997801, IsraelMore by Lihi Adler-Abramovichhttps://orcid.org/0000-0003-3433-0625, Stacey F. BentStacey F. BentDepartment of Chemical Engineering, Stanford University, Stanford, California 94305, United StatesMore by Stacey F. Benthttps://orcid.org/0000-0002-1084-5336, Fedwa El-MellouhiFedwa El-MellouhiQatar Environment and Energy Research Institute, Hamad Bin Khalifa University, Qatar Foundation, P.O. Box 34110, Doha, QatarMore by Fedwa El-Mellouhihttps://orcid.org/0000-0003-4338-9290, Eugenia KumachevaEugenia KumachevaDepartment of Chemistry, University of Toronto, 80 Saint George Street, Toronto, Ontario M5S 3H6, CanadaSCAMT Institute, ITMO University, 9 Lomonosova Street, Saint Petersburg 191002, Russian FederationDepartment of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, Ontario M5S 3E5, CanadaMore by Eugenia Kumachevahttps://orcid.org/0000-0001-5942-3890, Delia J. MillironDelia J. MillironMcKetta Department of Chemical Engineering, The University of Texas at Austin, 200 E. Dean Keeton Street, Austin, Texas 78712, United StatesMore by Delia J. Millironhttps://orcid.org/0000-0002-8737-451X, Jennifer NeuJennifer NeuNational High Magnetic Field Laboratory, Tallahassee, Florida 32310, United StatesMore by Jennifer Neu, Elham RezasoltaniElham RezasoltaniDepartment of Physics, Imperial College London, London SW7 2BX, United KingdomMore by Elham Rezasoltanihttps://orcid.org/0000-0003-0644-8263, Qing ShenQing ShenFaculty of Informatics and Engineering, The University of Electro-Communications, 1-5-1 Chofugaoka, Tokyo 182-8585, JapanMore by Qing Shenhttps://orcid.org/0000-0001-8359-3275, and Sabrina SicoloSabrina SicoloBASF SE, Carl-Bosch-Strasse 38, 67056 Ludwigshafen am Rhein, GermanyMore by Sabrina Sicolohttps://orcid.org/0000-0001-8575-3834Cite this: Chem. Mater. 2021, 33, 17, 6585–6588Publication Date (Web):August 24, 2021Publication History Published online24 August 2021Published inissue 14 September 2021https://pubs.acs.org/doi/10.1021/acs.chemmater.1c02648https://doi.org/10.1021/acs.chemmater.1c02648editorialACS PublicationsCopyright © Published 2021 by 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 Views5422Altmetric-Citations2LEARN 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 (732 KB) Get e-AlertscloseSUBJECTS:COVID-19,Lasers,Nanomaterials,Solar cells,Students 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 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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.168 | 0.026 |
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".