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Record W2796069345 · doi:10.1016/j.joule.2018.03.007

Photocatalytic Hydrogenation of Carbon Dioxide with High Selectivity to Methanol at Atmospheric Pressure

2018· article· en· W2796069345 on OpenAlexafffund
Lu Wang, Mireille Ghoussoub, Hong Wang, Yue Shao, Wei Sun, Athanasios A. Tountas, Thomas E. Wood, Hai Li, Joel Y. Y. Loh, Yuchan Dong, Meikun Xia, Young Li, Shenghua Wang, Jia Jia, Chenyue Qiu, Chenxi Qian, Nazir P. Kherani, Le He, Xiaohong Zhang, Geoffrey A. Ozin

Bibliographic record

VenueJoule · 2018
Typearticle
Languageen
FieldChemical Engineering
TopicCatalysts for Methane Reforming
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaQinglan Project of Jiangsu Province of ChinaCollaborative Innovation Center of Suzhou Nano Science and TechnologyEnvironment and Climate Change CanadaMaterials Research Institute, Pennsylvania State UniversityMinistero dello Sviluppo EconomicoPriority Academic Program Development of Jiangsu Higher Education InstitutionsNational Natural Science Foundation of ChinaConnaught FundNational Key Research and Development Program of ChinaHigher Education Discipline Innovation ProjectNatural Science Foundation of Jiangsu ProvinceOntario Ministry of Research, Innovation and Science
KeywordsPhotocatalysisCarbon dioxideMethanolAtmospheric pressureSelectivityHigh pressureMaterials scienceChemical engineeringPhotochemistryEnvironmental scienceEnvironmental chemistryChemistryOrganic chemistryCatalysisMeteorologyEngineering physicsEngineering

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.007
GPT teacher head0.222
Teacher spread0.215 · 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 source (direct Gemma or distilled Codex), 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

Citations226
Published2018
Admission routes2
Has abstractno

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