MétaCan
Menu
Back to cohort
Record W2588580214

Biofuels Markets and Policies in Ukraine

2017· preprint· en· W2588580214 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueLA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · 2017
Typepreprint
Languageen
FieldAgricultural and Biological Sciences
TopicAgriculture Market Analysis Ukraine
Canadian institutionsnot available
FundersGrantová Agentura České RepublikyEuropean CommissionMcGill University
KeywordsBiofuelBusinessEnergy securityProduction (economics)Fossil fuelNatural resource economicsLegislatureRaw materialEnergy policyInternational tradeEconomicsEconomic policyAgricultural economicsRenewable energyEngineeringWaste managementPolitical science
DOInot available

Abstract

fetched live from OpenAlex

This paper provides an overview of biofuel’s markets Ukraine. While Ukraine has great competitive advantage in the production of biofuels based on availability of the feedstock and fertile soils, it does not utilize this opportunity despite the policy goal of decreasing energy dependence on Russian fossil fuels. In the recent years Ukraine was working on fulfilment of European standards in the sector of biofuels. Most importantly, as opposed to Russia, Ukraine has built legislative base which aims to support the industry development and offer large scale of benefits. But due to high excise duty, low oil prices and no penalties for not achieving established indicators, the biofuel industry still stays non-operating.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.716
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.002
Research integrity0.0010.001
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 head0.022
GPT teacher head0.242
Teacher spread0.220 · 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