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HYDROGEN ENERGY FOR A CLEANER ENVIRONMENT

2005· article· en· W1981918817 on OpenAlexaff
Tungadri Bose, Pierre Bénard, A. Hourri

Bibliographic record

VenueClean Air · 2005
Typearticle
Languageen
FieldEnergy
TopicHybrid Renewable Energy Systems
Canadian institutionsUniversité du Québec à Trois-Rivières
Fundersnot available
KeywordsFossil fuelHydrogen economyEnergy (signal processing)HydrogenHydrogen fuelWarning signsEnvironmental scienceHydrogen productionEnergy carrierClean energyEnvironmental economicsBusinessComputer scienceFuel cellsNatural resource economicsWaste managementEngineeringEnvironmental protectionChemistryEconomicsPhysicsChemical engineeringTransport engineering

Abstract

fetched live from OpenAlex

At the onset of this new century, we are facing the early warning signs of an impending global environmental crisis. We are also becoming aware of the finite nature of our fossil fuel sources and their uneven distribution. These challenges favor a transition to a new energy system based on hydrogen, a clean energy vector. In this article, we discuss the challenges to the introduction of hydrogen on the energy market: production, infrastructure, safety, with special emphasis on hydrogen storage, as well as the solutions proposed to overcome them.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.870
Threshold uncertainty score0.967

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.001

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 head0.011
GPT teacher head0.206
Teacher spread0.195 · 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 designNot applicable
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

Citations1
Published2005
Admission routes1
Has abstractyes

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