More of EPA’s SPARC Online Calculator−The Need for High-Quality Predictions of Chemical Properties
Bibliographic record
Abstract
ADVERTISEMENT RETURN TO ISSUEPREVViewpointNEXTMore of EPA's SPARC Online Calculator−The Need for High-Quality Predictions of Chemical PropertiesAuthors' ViewpointHans Peter H. Arp*, Steven T. J. Droge*, Satoshi Endo*, Walter Giger, Kai-Uwe Goss, Steven B. Hawthorne, Scott A. Mabury, Philipp Mayer, Michael S. Mclachlan, James F. Pankow, René P. Schwarzenbach, Frank Wania, and Baoshan XingView Author Information Norwegian Geotechnical Institute, Oslo UFZ Helmholtz Centre for Environmental Research, Leipzig, Germany UFZ Helmholtz Centre for Environmental Research, Leipzig, Germany Swiss Federal Institute of Aquatic Science and Technology (EAWAG), Dübendorf, Switzerland Giger Research Consulting (GRC), Zurich, Switzerland UFZ Helmholtz Centre for Environmental Research, Leipzig, Germany Energy and Environmental Research Center, University of North Dakota, Grand Forks University of Toronto, Canada Aarhus University, Roskilde, Denmark Stockholm University, Sweden Portland State University, Oregon Swiss Federal Institute of Technology Zurich University of Toronto Scarborough, Canada University of Massachusetts, Amherst* Please address correspondence regarding this Viewpoint to [email protected], [email protected], and/or [email protected]Cite this: Environ. Sci. Technol. 2010, 44, 12, 4400–4401Publication Date (Web):May 18, 2010Publication History Received8 February 2010Published online18 May 2010Published inissue 15 June 2010https://pubs.acs.org/doi/10.1021/es100437ghttps://doi.org/10.1021/es100437gnewsACS PublicationsCopyright © 2010 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 Views2953Altmetric-Citations18LEARN 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 (689 KB) Get e-AlertscloseSUBJECTS:Calibration,Molecular modeling,Quality management Get e-Alerts
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".