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Record W4401036917 · doi:10.5376/jeb.2024.15.0005

The Dual Role of Agricultural Products as Food and Fuel: Energy Conversion and Utilization

2024· article· en· W4401036917 on OpenAlexvenueno aff
Wenzhong Huang

Bibliographic record

VenueJournal of Energy Bioscience · 2024
Typearticle
Languageen
FieldEnergy
TopicGlobal Energy and Sustainability Research
Canadian institutionsnot available
Fundersnot available
KeywordsDual (grammatical number)AgricultureBusinessDual purposeDual roleEnvironmental scienceNatural resource economicsWaste managementAgricultural economicsEconomicsEngineeringChemistryGeographyMechanical engineering

Abstract

fetched live from OpenAlex

This study explores and elucidates the dual role of agricultural products as both food and fuel, examining the processes of energy conversion and utilization, and providing a comprehensive analysis of how agricultural products can sustainably meet the dual demands of nutrition and energy. The study identifies key findings that highlight the significant nutritional value and benefits of agricultural products, their role in food security, and the sustainability of agricultural practices. It investigates the types and sources of biofuels, the energy content and efficiency of biofuel production, and the environmental impacts associated with their use, incorporating case studies to showcase successful integrated food and fuel systems while highlighting the complexities of balancing these dual roles. The study also discusses emerging technologies in energy conversion, the potential of genetically modified crops, and the prospects for sustainable food-fuel systems. The results indicate that integrating advanced technologies, sustainable agricultural practices, and supportive policy frameworks is essential for optimizing the dual role of agricultural products. By addressing land use conflicts, enhancing crop selection, and promoting stakeholder engagement, it is possible to develop resilient systems that provide both food and energy, thereby contributing to global sustainability goals.

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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.767
Threshold uncertainty score0.237

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.012
GPT teacher head0.250
Teacher spread0.238 · 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 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

Citations2
Published2024
Admission routes1
Has abstractyes

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