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Record W2029651020 · doi:10.1039/c4ta04495a

Mapping the performance of amorphous ternary metal oxide water oxidation catalysts containing aluminium

2014· article· en· W2029651020 on OpenAlexafffund
Cuijuan Zhang, Randal D. Fagan, Rodney D. L. Smith, Stephanie A. Moore, Curtis P. Berlinguette, Simon Trudel

Bibliographic record

VenueJournal of Materials Chemistry A · 2014
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsUniversity of British ColumbiaCalgary Laboratory ServicesVancouver Biotech (Canada)University of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsUniversity of CalgaryAlfred P. Sloan Foundation
KeywordsTernary operationCatalysisAmorphous solidCobaltAluminiumOxygen evolutionNickelOxygenMaterials scienceInorganic chemistryStoichiometryOxideMetalAluminium oxideCobalt oxideChemical engineeringChemistryMetallurgyPhysical chemistryOrganic chemistryElectrochemistry

Abstract

fetched live from OpenAlex

Ternary amorphous catalysts containing varying stoichiometries of aluminium, iron, nickel and/or cobalt for the oxygen evolution reaction (OER) were mapped out to define the optimal composition for mediating oxygen evolution from water.

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)0.001
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 score0.003
Threshold uncertainty score0.453

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.009
GPT teacher head0.198
Teacher spread0.189 · 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

Citations53
Published2014
Admission routes2
Has abstractyes

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