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Record W3177860134 · doi:10.1039/d1ee00870f

Seawater electrolysis for hydrogen production: a solution looking for a problem?

2021· article· en· W3177860134 on OpenAlexafffund
Mohd Adnan Khan, Tareq A. Al‐Attas, Soumyabrata Roy, Muhammad M. Rahman, Noreddine Ghaffour, Venkataraman Thangadurai, Steve Larter, Jinguang Hu, Pulickel M. Ajayan, Md Golam Kibria

Bibliographic record

VenueEnergy & Environmental Science · 2021
Typearticle
Languageen
FieldEnergy
TopicHybrid Renewable Energy Systems
Canadian institutionsUniversity of Calgary
FundersCanada First Research Excellence Fund
KeywordsSeawaterElectrolysisDesalinationHydrogen productionProduction (economics)Environmental scienceElectrolysis of waterPerspective (graphical)OceanographyHydrogenComputer scienceChemistryEconomicsGeologyMembrane

Abstract

fetched live from OpenAlex

This study assesses research and development needs for direct seawater electrolysis from energy, cost and environmental aspects and presents a forward-looking perspective on future R&D priorities in desalination and electrolysis technologies.

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)0.004
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.013
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0020.003
Scholarly communication0.0070.017
Open science0.0020.004
Research integrity0.0060.005
Insufficient payload (model declined to judge)0.0130.004

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 head0.203
Teacher spread0.196 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations517
Published2021
Admission routes2
Has abstractyes

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