Bibliographic record
Abstract
Biofuels are renewable energy used in transportation as a substitute and/or complement to fossil fuels. The application of these types of biofuels in transport may be in pure form, that is, 100 per cent of the fuel is bio-based and/or blended where a percentage of the fuel is renewable. For example, E15 or B15 means a 15 per cent blending of ethanol or biodiesel with fossil-based fuel. The two major types of biofuels currently produced are ethanol and biodiesel. These fuels are derived from biomass or waste. The major producers of biofuel are the United States (US), Brazil, the European Union (EU), China, Canada and India. Biofuels are expected to offer these countries improved energy security, a reduction in externalities that negatively impact the environment and rural development opportunities. Further, countries, particularly developing countries, may benefit from biofuels through an opportunity to supply a number of major nations that have mandated consumption, such as the US and EU. One aspect of the production of biofuels is that it diverts productive agricultural land out of food production. As a result, food security may decline due to rising food prices, particularly for the very poor. Hence, there are potential negative externalities associated with biofuel production. A paradigm shift toward the encouragement of the development of biofuels industries took place in a number of countries before the negative externalities became apparent.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".