MétaCan
Menu
Back to cohort
Record W3117675382 · doi:10.47703/ejebs.v4i58.24

Liquid Biofuels: Sustainable Development Analysis

2020· article· en· W3117675382 on OpenAlexaff
Bulcsú Reményik, László Vasa, Lóránt Dénes Dávid, Imre Varga

Bibliographic record

VenueEurasian Journal of Economic and Business Studies · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicHungarian Social, Economic and Educational Studies
Canadian institutionsSavaria (Canada)
Fundersnot available
KeywordsBiofuelAgricultureNatural resource economicsAviation biofuelAgricultural economicsSubsidyMultinational corporationBusinessProduction (economics)Competition (biology)PetroleumEcological footprintFossil fuelGreenhouse gasEnvironmental scienceSustainable developmentEconomicsBioenergyWaste managementEngineeringGeographyEcologyMarket economy

Abstract

fetched live from OpenAlex

The ecological footprint of Hungary is close to the European average and we expect further growth. The projects of the Széchenyi 2020 program and the Hungarian Multinational Oil and Gas Company (MOL) promote the development of the green economy provide significant subsidies. The depletion of petroleum-derived fuel and environmental concern has promoted to look over the biofuel as an alternative fuel source. However, the production of biofuels is an expensive process. The rapid spread of biofuels created an agricultural expansion, contributing to rising water demands; however, that was already a serious international problem. The competition for agricultural areas has an impact of price increment because the excessive rate of energy crops can replace not only the same kinds of food crops but other (for example fodder) varieties. In our evaluation, the third generation of biofuels seems the ultimate solution for us in the following 25-30 years’ period.

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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.005
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0120.001

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 head0.064
GPT teacher head0.321
Teacher spread0.257 · 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 designSimulation or modeling
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

Citations3
Published2020
Admission routes1
Has abstractyes

Explore more

Same venueEurasian Journal of Economic and Business StudiesSame topicHungarian Social, Economic and Educational StudiesFrench-language works237,207