Energy Spotlight
Bibliographic record
Abstract
ADVERTISEMENT RETURN TO ISSUEPREVEnergy FocusNEXTEnergy SpotlightPersonal Reflections of Energy Researchers on the 2019 Chemistry Nobel LaureatesPrashant V. KamatPrashant V. KamatUniversity of Notre Dame, Notre Dame, Indiana 46556, United StatesMore by Prashant V. Kamathttp://orcid.org/0000-0002-2465-6819, Louis F. J. PiperLouis F. J. PiperBinghamton University, Binghamton, New York 13902, United StatesMore by Louis F. J. Piperhttp://orcid.org/0000-0002-3421-3210, Arumugam ManthiramArumugam ManthiramMaterials Science & Engineering Program and Texas Materials Institute, The University of Texas at Austin, Austin, Texas 78712, United StatesMore by Arumugam Manthiramhttp://orcid.org/0000-0003-0237-9563, Shigeto OkadaShigeto OkadaKyushu University, Fukuoka 819-0395, JapanMore by Shigeto Okada, M. Saiful IslamM. Saiful IslamDepartment of Chemistry, University of Bath, Bath BA2 7AY, United KingdomMore by M. Saiful Islamhttp://orcid.org/0000-0003-3882-0285, Ying Shirley MengYing Shirley MengUniversity of California, San Diego, La Jolla, California 92093, United StatesMore by Ying Shirley Menghttp://orcid.org/0000-0001-8936-8845, Xiaolin LiXiaolin LiPacific Northwest National Laboratory, Richland, Washington 99354, United StatesMore by Xiaolin Li, Bryan D. McCloskeyBryan D. McCloskeyUniversity of California and Lawrence Berkeley National Laboratory, Berkeley, California 94720, United StatesMore by Bryan D. McCloskeyhttp://orcid.org/0000-0001-6599-2336, Yang-Kook SunYang-Kook SunHanyang University, Seoul, KoreaMore by Yang-Kook Sunhttp://orcid.org/0000-0002-0117-0170, Linda NazarLinda NazarDepartment of Chemistry, University of Waterloo, Waterloo, Ontario N2L 3G1, CanadaMore by Linda Nazarhttp://orcid.org/0000-0002-3314-8197, and Sarbajit BanerjeeSarbajit BanerjeeTexas A&M University, College Station, Texas 77843, United StatesMore by Sarbajit Banerjeehttp://orcid.org/0000-0002-2028-4675Cite this: ACS Energy Lett. 2019, 4, 11, 2763–2769Publication Date (Web):October 25, 2019Publication History Received18 October 2019Accepted18 October 2019Published online25 October 2019Published inissue 8 November 2019https://pubs.acs.org/doi/10.1021/acsenergylett.9b02290https://doi.org/10.1021/acsenergylett.9b02290article-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 Views3019Altmetric-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 (2 MB) Get e-AlertscloseSUBJECTS:Batteries,Electrodes,Energy,Materials,Oxides 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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; both teacher heads agree on what is shown here.
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".