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Record W4239891291 · doi:10.1021/bk-2012-1092.pr001

Preface

2012· other· en· W4239891291 on OpenAlexaffabout
Ajay K. Dalai

Bibliographic record

VenueACS symposium series · 2012
Typeother
Languageen
FieldEnergy
TopicCO2 Reduction Techniques and Catalysts
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsCitationLibrary scienceWorld Wide WebComputer scienceEngineering

Abstract

fetched live from OpenAlex

ADVERTISEMENT RETURN TO BOOKPREVChapterNEXTPreface Ajay K. DalaiDr. Ajay K. Dalai Associate Dean, Research and Partnerships Professor and Canada Research Chair in Bio-Energy and Environmentally Friendly Chemical Processing College of Engineering, University of Saskatchewan 57 Campus Drive, Saskatoon, SK Canada S7N 5A9 [email protected] ; (306) 966-4768/4771 ; (306) 966-4777 http://www.engr.usask.ca/~dalai More by Dr. Ajay K. DalaiDOI: 10.1021/bk-2012-1092.pr001 This publication is free to access through this site. Learn MorePublication Date (Web):January 20, 2012Publication History Published online20 January 2012Published inprint 1 January 2012Request reuse permissions Copyright © 2012 American Chemical Society. This publication is available under these Terms of Use. Nanocatalysis for Fuels and Chemicalsp ixACS Symposium SeriesVol. 1092ISBN13: 9780841226852eISBN: 9780841226869Chapter Views912Citations-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 (95 KB) Get e-Alerts

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.026
Threshold uncertainty score1.000

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)0.0080.001

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 head0.009
GPT teacher head0.228
Teacher spread0.219 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2012
Admission routes2
Has abstractyes

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