BIODIESEL: EVALUATING THE RISK OF SOYBEAN OIL SHORTAGE AND THE CONTRIBUTION OF BRAZIL TO THE GLOBAL SUPPLY
Bibliographic record
Abstract
The growing demand for biodiesel around the world will provide a substantial boost to the price of vegetable oil and raise the question of fuel security over food security, as it is already happening with sugar and corn in the case of ethanol. However, the potential of higher prices to boost production is limited by structural constraints in the agricultural sector. Huge redistribution of land uses seems inevitable but will be difficult to achieve in the short term. Therefore, policies aiming to promote aggressive production of biofuels may focus on the multidimensional competition for land. High vegetable oil prices will stimulate the production of high oil content oilseeds worldwide. Yet this solution can only be developed in the medium term. Developing new crops of palm trees or jatropha takes a minimum of 3-4 years from seeding to the first harvest, and 8-10 years to achieve full maturity. Only by speeding up major technological breakthroughs may these constraints eventually be overcome. In this context, the only two reasonable candidates with available land and mature oilseed industry, and that have the potential to meet the short-term and skyrocketing global demand for biofuels, are Canada and mainly Brazil. These countries are believed to still have large areas of available and productive land that can quickly be allocated to biofuel production. Indeed, in the case of Brazil, the remaining arable lands emerge as the planet’s last agricultural frontier. The paper examines Brazil’s potential role as a leading global supplier of biofuels.
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 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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.000 |
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.
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".