Response to Comment on “More of EPA’s SPARC Online Calculator—The Need for High Quality Predictions of Chemical Properties”
Bibliographic record
Abstract
ADVERTISEMENT RETURN TO ISSUEPREVLetterNEXTResponse to Comment on "More of EPA's SPARC Online Calculator—The Need for High Quality Predictions of Chemical Properties"Hans Peter H. Arp, Steven T. J. Droge, Satoshi Endo, Kai-Uwe Goss, Walter Giger, 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, Norway UFZ Helmholtz Centre for Environmental Research, Leipzig, Germany Giger Research Consulting (GRC), Zurich, Switzerland and Swiss Federal Institute of Aquatic Science and Technology (Eawag), Dübendorf, Switzerland Energy and Environmental Research Center, University of North Dakota, Grand Forks, North Dakota University of Toronto, Ontario, Canada Aarhus University, Roskilde, Denmark Stockholm University, Sweden Portland State University, Portland, Oregon Swiss Federal Institute of Technology Zurich, Switzerland University of Toronto Scarborough, Ontario, Canada University of Massachusetts, Amherst, Massachusetts[email protected][email protected][email protected]Cite this: Environ. Sci. Technol. 2010, 44, 20, 7746–7747Publication Date (Web):September 10, 2010Publication History Published online10 September 2010Published inissue 15 October 2010https://pubs.acs.org/doi/10.1021/es102925shttps://doi.org/10.1021/es102925sletterACS PublicationsCopyright © 2010 American Chemical SocietyRequest reuse permissionsArticle Views662Altmetric-Citations3LEARN 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 Other access optionsGet e-Alertsclose SUBJECTS:Calibration,Environmental pollution,Interfaces,Quality management,Theoretical and computational chemistry 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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.012 | 0.007 |
| Insufficient payload (model declined to judge) | 0.372 | 0.285 |
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".