Using Infrared and Raman Spectroscopy to Analyze Gas–Solid Reactions
Bibliographic record
Abstract
Research Article| November 01, 2018 Using Infrared and Raman Spectroscopy to Analyze Gas–Solid Reactions Terrence P. Mernagh; Terrence P. Mernagh Research School of Earth Sciences, Australian National University, Canberra ACT 2601, Australia Search for other works by this author on: GSW Google Scholar Penelope L. King; Penelope L. King Research School of Earth Sciences, Australian National University, Canberra ACT 2601, Australia Search for other works by this author on: GSW Google Scholar Paul F. McMillan; Paul F. McMillan Department of Chemistry, Christopher Ingold Laboratories, University College London, 20 Gordon Street London WC1H 0AJ, UK Search for other works by this author on: GSW Google Scholar Jeff. A. Berger; Jeff. A. Berger Department of Physics, University of Guelph, Guelph ON N1G 2W1, Canada Search for other works by this author on: GSW Google Scholar Kim N. Dalby Kim N. Dalby Department of Chemistry, University of Copenhagen, Universitetsparken 5, 2100 Copenhagen Denmark Search for other works by this author on: GSW Google Scholar Author and Article Information Terrence P. Mernagh Research School of Earth Sciences, Australian National University, Canberra ACT 2601, Australia Penelope L. King Research School of Earth Sciences, Australian National University, Canberra ACT 2601, Australia Paul F. McMillan Department of Chemistry, Christopher Ingold Laboratories, University College London, 20 Gordon Street London WC1H 0AJ, UK Jeff. A. Berger Department of Physics, University of Guelph, Guelph ON N1G 2W1, Canada Kim N. Dalby Department of Chemistry, University of Copenhagen, Universitetsparken 5, 2100 Copenhagen Denmark Publisher: Mineralogical Society of America First Online: 09 Nov 2018 Copyright © 2018 by the Mineralogical Society of AmericaMineralogical Society of America Reviews in Mineralogy and Geochemistry (2018) 84 (1): 177–228. https://doi.org/10.2138/rmg.2018.84.5 Article history First Online: 09 Nov 2018 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Search Site Citation Terrence P. Mernagh, Penelope L. King, Paul F. McMillan, Jeff. A. Berger, Kim N. Dalby; Using Infrared and Raman Spectroscopy to Analyze Gas–Solid Reactions. Reviews in Mineralogy and Geochemistry 2018;; 84 (1): 177–228. doi: https://doi.org/10.2138/rmg.2018.84.5 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietyReviews in Mineralogy and Geochemistry Search Advanced Search Gas–solid reactions result in changes in solid structure and composition including the formation of surface layers or thin films (Delmelle et al. 2018, this volume; Henley and Seward 2018, this volume; King et al. 2018, this volume; Palm et al 2018, this volume; Renggli and King 2018, this volume), dissolution-reprecipitation processes resulting in zoning or porosity and mineral replacement (Altree-Williams et al. 2015), formation of fluid/melt inclusions (Samson et al. 2003; Webster 2006), and new metamorphic mineral assemblages (Skippen and Marshall 1991; Henley et al. 2017).... You do not have access to this content, please speak to your institutional administrator if you feel you should have access.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.009 |
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".