<i>ACS Materials Au:</i> Innovations in Bioengineering Webinar Recap and Call for Papers
Bibliographic record
Abstract
ADVERTISEMENT RETURN TO ISSUEEditorialNEXTACS Materials Au: Innovations in Bioengineering Webinar Recap and Call for PapersStephanie L. Brock*Stephanie L. BrockACS Materials Au, Department of Chemistry, Wayne State University, 5101 Cass Avenue, Detroit, Michigan 48202-3489, United States*Email: [email protected]More by Stephanie L. Brockhttps://orcid.org/0000-0002-0439-302X, Maryam BadvMaryam BadvDepartment of Biomedical Engineering, University of Calgary, 2500 University Drive NW, CalgaryAlberta T2N 1N4, CanadaMore by Maryam Badvhttps://orcid.org/0000-0003-2226-3533, Ali KhademhosseniAli KhademhosseniTerasaki Institute for Biomedical Innovation, 1018 Westwood Blvd., Los Angeles, California 90024, United StatesMore by Ali Khademhosseni, and Paul S. WeissPaul S. WeissCalifornia NanoSystems Institute, Department of Chemistry and Biochemistry, Department of Bioengineering, and Department of Materials Science and Engineering, University of California, Los Angeles, Los Angeles, California 90095, United StatesMore by Paul S. WeissCite this: ACS Mater. Au 2022, 2, 4, 381Publication Date (Web):July 13, 2022Publication History Published online13 July 2022Published inissue 13 July 2022https://doi.org/10.1021/acsmaterialsau.2c00048Copyright © Published 2022 by American Chemical SocietyRIGHTS & PERMISSIONSACS AuthorChoiceCC: Creative CommonsBY: Credit must be given to the creatorNC: Only noncommercial uses of the work are permittedND: No derivatives or adaptations of the work are permittedArticle Views272Altmetric-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 PDF (1 MB) Get e-AlertsSUBJECTS:Biology,Biomaterials,Chemical engineering and industrial chemistry,Gold,Materials 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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 0.000 |
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 teacher head, 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".