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Record W4394697567 · doi:10.5376/jeb.2024.15.0002

Sustainable Development Strategy of Bioenergy and Global Energy Transformation

2024· article· en· W4394697567 on OpenAlexvenueno aff
Sheengh Yu

Bibliographic record

VenueJournal of Energy Bioscience · 2024
Typearticle
Languageen
FieldEnergy
TopicGlobal Energy and Sustainability Research
Canadian institutionsnot available
Fundersnot available
KeywordsBioenergySustainable developmentTransformation (genetics)Natural resource economicsSustainable energyBusinessEnvironmental scienceAgroforestryEnvironmental economicsBiofuelRenewable energyEconomicsEngineeringEcologyBiologyWaste management

Abstract

fetched live from OpenAlex

Bioenergy, as a sustainable and clean energy source, plays an important role in sustainable development and global energy transformation. This review explores the role and significance of bioenergy in sustainable development, the challenges and issues facing the current development of bioenergy, and the development strategies of bioenergy in global energy transformation. The review discusses the role and significance of bioenergy in sustainable development, analyzes the challenges and issues facing the current development of bioenergy, proposes development strategies of bioenergy in global energy transformation, and forecasts the future development prospects of bioenergy. In the future, bioenergy will continuously improve production efficiency and reduce production costs; the sources of bioenergy raw materials will become more extensive, thus reducing competition for resources; the application scope of bioenergy will continue to expand, including transportation, construction, and other fields; and bioenergy will be combined with technologies from other fields to achieve diversified utilization of bioenergy.

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)0.001
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 score0.007
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0010.002
Scholarly communication0.0040.003
Open science0.0010.003
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0070.003

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 head0.013
GPT teacher head0.274
Teacher spread0.261 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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