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Record W276538972 · doi:10.1177/097639961200300104

Issues in Transport Biofuels Production: A Review of the Global Experience

2012· review· en· W276538972 on OpenAlexaff
A.S. Bhullar, Balbinder Deo

Bibliographic record

VenueMillennial Asia · 2012
Typereview
Languageen
FieldEngineering
TopicBiofuel production and bioconversion
Canadian institutionsUniversity of Northern British Columbia
Fundersnot available
KeywordsBiofuelProduction (economics)Aviation biofuelCommercializationRaw materialNatural resource economicsInvestment (military)IncentiveBusinessEconomicsEnvironmental economicsBioenergyEngineeringWaste managementMarketingEcologyMarket economy

Abstract

fetched live from OpenAlex

In the last few years ethanol and biodiesel as energy source have been seen as a panacea because of their carbon neutrality and transport oil substitution abilities. The biofuels production got the world wide response and the governments in a large number of countries took policy initiatives to promote biofuels, fixed blending mandates and designed and implemented the economic incentives for production and commercialization of biofuels. The evaluation of global biofuels production in the last few years brings out that the biofuels production will sustain only if the production of biofuels feedstock fits well in the whole food and agro-ecological system, avoids the other numerous limiting conditions and becomes a long term cost effective proven source. There is no standard package of practices for the choice of feedstock but it will depend on the assessment of each micro agro-ecological situation. The production of selected feedstock on commercial scale may require substantial investment of time and money in research and development for plant varieties improvement and evolving the suitable production technology. In the absence of such approach, the biofuels production programs are likely to be engulfed in one issue or the other and may bring unforeseen externalities.

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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.960
Threshold uncertainty score0.803

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.034
GPT teacher head0.295
Teacher spread0.261 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations0
Published2012
Admission routes1
Has abstractyes

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