Bibliographic record
Abstract
ADVERTISEMENT RETURN TO BOOKPREVChapterNEXTPrefaceMarion H. EmmertMarion H. Emmert Process Research & Development MRL Merck & Co., Inc. Rahway, New Jersey 07065, United States More by Marion H. Emmert, Matthieu JouffroyMatthieu Jouffroy Chemical Process R&D, Discovery Process Research Janssen Pharmaceutica N.V. Turnhoutseweg 30 2340 Beerse, Belgium More by Matthieu Jouffroy, and David C. LeitchDavid C. Leitch Department of Chemistry University of Victoria Victoria, British Columbia V8P 5C2, Canada More by David C. LeitchDOI: 10.1021/bk-2022-1420.pr001 This publication is free to access through this site. Learn MorePublication Date (Web):November 15, 2022Publication History Published online15 November 2022Request reuse permissions Copyright © 2022 American Chemical Society. This publication is available under these Terms of Use. The Power of High-Throughput Experimentation: Case Studies from Drug Discovery, Drug Development, and Catalyst Discovery (Volume 2)p ixACS Symposium SeriesVol. 1420ISBN13: 9780841297555eISBN: 9780841297548Chapter Views153Citations-LEARN ABOUT THESE METRICSChapter 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. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InRedditEmail PDF (1 MB) 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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.799 | 0.701 |
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".