Biofuel Feedstock Assessment for Selected Countries
Bibliographic record
Abstract
Findings from biofuel feedstock production assessments and projections of future supply are presented and discussed. The report aims to improve capabilities to assess the degree to which imported biofuel could contribute to meeting future U.S. targets to reduce dependence on imported oil. The study scope was focused to meet time and resource requirements. A screening process identified Argentina, Brazil, Canada, China, Colombia, India, Mexico, and the Caribbean Basin Initiative (CBI) region for initial analysis, given their likely role in future feedstock supply relevant to U.S. markets. Supply curves for selected feedstocks in these countries are projected for 2012, 2017 and 2027. The supply functions, along with calculations to reflect estimated supplies available for export and/or biofuel production, were provided to DOE for use in a broader energy market allocation study. Potential cellulosic supplies from crop and forestry residues and perennials were also estimated for 2017 and 2027. The analysis identified capacity to potentially double or triple feedstock production by 2017 in some cases. A majority of supply growth is derived from increasing the area cultivated (especially sugarcane in Brazil). This is supplemented by improving yields and farming practices. Most future supplies of corn and wheat are projected to be allocated to food and feed. Larger shares of future supplies of sugarcane, soybean and palm oil production will be available for export or biofuel. National policies are catalyzing investments in biofuel industries to meet targets for fuel blending that generally fall in the 5-10% range. Social and environmental concerns associated with rapid expansion of feedstock production are considered. If the 2017 projected feedstock supply calculated as ‘available’ for export or biofuel were converted to fuel, it would represent the equivalent of about 38 billion gallons of gasoline. Sugarcane and bagasse dominate the available supply, representing 64% of the total. Among the nations studied, Brazil is the source of about two-thirds of available supplies, followed distantly by Argentina (12%), India and the CBI region.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".