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Record W2809684303 · doi:10.1039/c7ee03639f

Pathways to electrochemical solar-hydrogen technologies

2018· article· en· W2809684303 on OpenAlexfundno aff
Shane Ardo, David Fernández Rivas, Miguel A. Modestino, Verena Schulze Greiving, Fatwa F. Abdi, Esther Alarcón‐Lladó, Vincent Artero, Katherine E. Ayers, Corsin Battaglia, Jan‐Philipp Becker, Dmytro Bederak, Alan Berger, Francesco Buda, Enrico Chinello, B. Dam, Valerio Di Palma, Tomas Edvinsson, Katsushi Fujii, Han Gardeniers, Hans Geerlings, S. Mohammad H. Hashemi, Sophia Haussener, Frances A. Houle, Jurriaan Huskens, Brian D. James, Kornelia Konrad, Akihiko Kudo, Pramod Patil Kunturu, Detlef Lohse, Bastian Mei, Eric L. Miller, Gary F. Moore, Jiri Muller, Katherine L. Orchard, Timothy E. Rosser, Fadl H. Saadi, J.W.A. Schüttauf, Brian Seger, Stafford W. Sheehan, Wilson A. Smith, Joshua M. Spurgeon, Maureen H. Tang, Roel van de Krol, Peter C. K. Vesborg, Pieter Westerik

Bibliographic record

VenueEnergy & Environmental Science · 2018
Typearticle
Languageen
FieldEnergy
TopicHybrid Renewable Energy Systems
Canadian institutionsnot available
FundersOffice of Energy EfficiencyOffice of ScienceDivision of Chemical, Bioengineering, Environmental, and Transport SystemsFuel Cell Technologies ProgramNederlandse Organisatie voor Wetenschappelijk OnderzoekAgence Nationale de la RechercheOffice of Energy Efficiency and Renewable EnergyU.S. Department of EnergyEuropean CommissionMinisterie van Onderwijs, Cultuur en WetenschapYork UniversitySchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungMinisterie van Economische ZakenVillum FondenNational Science Foundation
KeywordsElectrochemistryHydrogen technologiesMaterials scienceEnvironmental scienceHydrogenEngineering physicsNanotechnologyHydrogen productionChemistryEngineeringHydrogen economyElectrode

Abstract

fetched live from OpenAlex

Several application fields can benefit from solar-hydrogen technologies via specific short-term and long-term pathways.

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 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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.024
Threshold uncertainty score0.079

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.003
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0240.005

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 head0.192
Teacher spread0.186 · 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 designTheoretical or conceptual
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

Citations346
Published2018
Admission routes1
Has abstractyes

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