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Record W4407559425 · doi:10.1039/d4ee06191h

Methane pyrolysis for hydrogen production: navigating the path to a net zero future

2025· article· en· W4407559425 on OpenAlexafffund
Alireza Lotfollahzade Moghaddam, Seyedsina Hejazi, Moslem Fattahi, Md Golam Kibria, W. Murray Thomson, Rashed Aleisa, Mohd Adnan Khan

Bibliographic record

VenueEnergy & Environmental Science · 2025
Typearticle
Languageen
FieldChemical Engineering
TopicCatalysts for Methane Reforming
Canadian institutionsUniversity of TorontoUniversity of CalgaryUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaAlberta Innovates
KeywordsHydrogen productionPath (computing)Net (polyhedron)Production (economics)PyrolysisMethaneZero (linguistics)Environmental scienceHydrogenWaste managementChemistryComputer scienceEconomicsEngineeringMathematicsComputer networkMicroeconomicsOrganic chemistryPhilosophy

Abstract

fetched live from OpenAlex

Hydrogen is a versatile energy vector, pivotal to a NetZero future, but will require the development of technologies for low-carbon hydrogen production like methane pyrolysis and new value chains that connect supply to demand.

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)0.002
metaresearch head score (Gemma)0.003
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: Review · Consensus signal: none
Teacher disagreement score0.011
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.003
Scholarly communication0.0040.011
Open science0.0010.004
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0110.003

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 head0.004
GPT teacher head0.224
Teacher spread0.219 · 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
GenreReview

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

Citations108
Published2025
Admission routes2
Has abstractyes

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