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Record W2900399161 · doi:10.2138/rmg.2018.84.5

Using Infrared and Raman Spectroscopy to Analyze Gas–Solid Reactions

2018· article· en· W2900399161 on OpenAlexaffabout
Terrence P. Mernagh, P. L. King, Paul F. McMillan, J. A. Berger, Kim N. Dalby

Bibliographic record

VenueReviews in Mineralogy and Geochemistry · 2018
Typearticle
Languageen
FieldEngineering
TopicGas Sensing Nanomaterials and Sensors
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsLibrary scienceMedia studiesHistoryArt historySociologyComputer science

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.191
Threshold uncertainty score0.551

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.020
GPT teacher head0.287
Teacher spread0.267 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2018
Admission routes2
Has abstractyes

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