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Record W2160820034 · doi:10.3390/agriculture2040436

The Policy Objectives of a Biofuel Industry in Canada: An Assessment

2012· article· en· W2160820034 on OpenAlexaffabout
Danny Le Roy, K. K. Klein

Bibliographic record

VenueAgriculture · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Impact and Sustainability
Canadian institutionsUniversity of Lethbridge
Fundersnot available
KeywordsBiofuelAgricultural economicsSubsidyNatural resource economicsBusinessStock (firearms)Greenhouse gasProduction (economics)Ethanol fuelDiversification (marketing strategy)Aviation biofuelLivestockEconomicsAgricultureBioenergyBiotechnologyForestryMarket economyGeography

Abstract

fetched live from OpenAlex

Canada has a huge stock of biomass resources, which provides a basis (and a temptation) for development of a major bio-fuels industry. Both federal and provincial governments have engaged in a wide array of subsidies, mandates, and other measures to stimulate production and consumption of biofuels. As a result, biofuels has become a growth industry in Canada with production of ethanol almost 10 times higher than it was ten years earlier. However, this has come at considerable cost to taxpayers. Increased biofuel production has resulted in minimal reduction in greenhouse gases, short run (but not long run) increases in net farm income (that benefited grain and oilseed producers but hurt livestock producers), large increases in the prices of farm land due to the higher grain and oilseed prices, and minimal impacts on rural economic diversification.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.791
Threshold uncertainty score0.917

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.006
Science and technology studies0.0040.001
Scholarly communication0.0070.002
Open science0.0010.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.005
GPT teacher head0.260
Teacher spread0.255 · 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 designQualitative
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

Citations13
Published2012
Admission routes2
Has abstractyes

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