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Record W4404866897 · doi:10.54254/2754-1169/2024.17718

Hydrogen Horizons: Navigating the Future of Renewable Energy

2024· article· en· W4404866897 on OpenAlexaff

Bibliographic record

VenueAdvances in Economics Management and Political Sciences · 2024
Typearticle
Languageen
FieldEnergy
TopicHybrid Renewable Energy Systems
Canadian institutionsSurrey Memorial Hospital
Fundersnot available
KeywordsNew horizonsRenewable energyEnvironmental scienceNatural resource economicsEconomicsEngineeringElectrical engineeringAerospace engineering

Abstract

fetched live from OpenAlex

With the pressing need to reduce carbon emissions from energy systems and lessen the effects of climate change, hydrogen energy emerges as a flexible and sustainable replacement for fossil fuels. This essay examines the potential of hydrogen energy as a transformative solution in the global quest for sustainable and clean energy sources. It is commonly known that reliable renewable energy sources like hydrogen are critical for a sustainable future. Therefore, it is important to understand the production methods, applications, and technological advancements in hydrogen energy. By discussing the current energy crisis, the hydrogen economy, and world-leading hydrogen businesses, this paper provides a holistic view of hydrogen energy.

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)0.002
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: Empirical · Consensus signal: none
Teacher disagreement score0.007
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.003
Scholarly communication0.0060.010
Open science0.0010.003
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0070.001

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 head0.007
GPT teacher head0.249
Teacher spread0.242 · 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
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

Citations0
Published2024
Admission routes1
Has abstractyes

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