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Record W2728429789 · doi:10.1111/sjoe.12177

Long‐Run Impact of Biofuels on Food Prices

2016· article· en· W2728429789 on OpenAlexfundno aff
Ujjayant Chakravorty, Marie‐Hélène Hubert, Michel Moreaux, Linda Nøstbakken

Bibliographic record

VenueScandinavian Journal of Economics · 2016
Typearticle
Languageen
FieldEnergy
TopicEnergy, Environment, and Transportation Policies
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsBiofuelEconomicsAgricultural economicsGasolineMandateAgricultureFood pricesDifferential (mechanical device)Natural resource economicsFood securityGeographyBiotechnologyWaste management

Abstract

fetched live from OpenAlex

Abstract About 40 percent of US corn is now used to produce biofuels, which are used as substitutes for gasoline in transportation. In this paper, we use a Ricardian model with differential land quality to show that world food prices could rise by about 32 percent by 2022. About half of this increase is from the biofuel mandate and the rest is a result of demand‐side effects in the form of population growth and income‐induced changes in dietary preferences, from cereals to meat and dairy products. However, aggregate world carbon emissions would increase, because of significant land conversion to farming and leakage from lower oil prices.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.088

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0110.001

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 head0.017
GPT teacher head0.239
Teacher spread0.221 · 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 designObservational
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

Citations33
Published2016
Admission routes1
Has abstractyes

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