Environmental risks and opportunities of biofuels
Bibliographic record
Abstract
Bioenergy refers to energy products derived from biomass -including heat, electricity and biofuels, the latter term referring to liquid fuels derived from biomass, particularly ethanol and biodiesel.Biofuels are generally used for transport, though they may also be used for generation of electricity.A few countries have a long history of biofuel use: in Brazil, ethanol from sugar cane has been promoted since 1975 (40 years ago). 1 The production of biofuels has expanded dramatically in recent decades.In 2013, 87.2 billion litres of ethanol, 26.3 billion litres of biodiesel and 3 billion litres of hydro-treated vegetable oils were produced globally, representing 2.3% of the use of transport fuels worldwide. 2 The major ethanol producers are the USA (50.3 billion litres), Brazil (25.5 billion litres), China (2.0 billion litres), Canada (1.8 billion litres) and France (1.0 billion litres), while the largest biodiesel producers are the USA (4.8 billion litres), Germany (3.1 billion litres), Argentina (2.9 billion litres), Brazil (2.3 billion litres), France (2.0 billion litres) and Indonesia (2.0 billion litres). 3 Most biofuels used currently are produced from starch, sugar or oil crops that have traditionally been grown for food.These are "firstgeneration" biofuels.Research and development of biofuels are now focused on "second-generation" or "advanced" biofuels, produced from lignocellulosic crops such as grasses and woody plants.The growth of the biofuel industry has largely been driven by policies 1 Dufey, A.,
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".