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Record W2791379910 · doi:10.1021/acs.iecr.8b00412

Response to “Comments on ‘Evaluation of the Performance of Newly Developed Demulsifiers on Dilbit Dehydration, Demineralization, and Hydrocarbon Losses to Tailings’”

2018· article· en· W2791379910 on OpenAlexaffabout
Ishpinder Kailey, Jacqueline A. Behles

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2018
Typearticle
Languageen
FieldEngineering
TopicMembrane-based Ion Separation Techniques
Canadian institutionsBaker Hughes (Canada)
Fundersnot available
KeywordsNoticeCitationInformation retrievalTrustworthinessComputer scienceLibrary sciencePolitical scienceInternet privacy

Abstract

fetched live from OpenAlex

ADVERTISEMENT RETURN TO ISSUEPREVCorrespondenceNEXTORIGINAL ARTICLEThis notice is a correctionResponse to “Comments on ‘Evaluation of the Performance of Newly Developed Demulsifiers on Dilbit Dehydration, Demineralization, and Hydrocarbon Losses to Tailings’”Ishpinder Kailey*Ishpinder KaileyBaker Hughes, 7020 45th Street, Leduc, Alberta Canada T9E 7E7*Tel.: (780) 980-5978. Fax: (780) 980-5989. E-mail: [email protected]More by Ishpinder Kaileyhttp://orcid.org/0000-0001-8731-1618 and Jacqueline BehlesJacqueline BehlesBaker Hughes, 7020 45th Street, Leduc, Alberta Canada T9E 7E7More by Jacqueline BehlesCite this: Ind. Eng. Chem. Res. 2018, 57, 12, 4476–4478Publication Date (Web):March 16, 2018Publication History Published online16 March 2018Published inissue 28 March 2018https://doi.org/10.1021/acs.iecr.8b00412Copyright © 2018 American Chemical SocietyRIGHTS & PERMISSIONSArticle Views560Altmetric-Citations-LEARN 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 InReddit PDF (1 MB) Get e-AlertsSUBJECTS:Clay,Interfaces,Magnetite,Minerals,Physical and chemical processes 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)0.002
metaresearch head score (Gemma)0.003
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 score0.107
Threshold uncertainty score0.626

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.102
GPT teacher head0.356
Teacher spread0.254 · 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

Citations0
Published2018
Admission routes2
Has abstractyes

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