Will biomass be used for bioenergy or transportationbiofuels? What drivers will influence biomass allocation
Bibliographic record
Abstract
Potential competition for biomass for current and future bioenergy/biofuel uses in Brazil, Denmark, Sweden and the USA were compared. In each of these countries, bioenergy and biofuels are already important in their energy mix. However, there is limited competition for biomass between bioenergy (heat/power/residential/industrial) and transportation biofuel applications. This situation is likely to continue until advanced biofuel technology becomes much more commercially established. In each of these countries, biomass is predominantly used to produce bioenergy, even in those regions where biofuels are significant component of their transportation sector (Brazil, Sweden and USA). The vast majority of biofuel production continues to be based on sugar, starch and oil rich feedstocks, while bioenergyis produced almost exclusively from forest biomass with agricultural biomass having a small, but increasing, secondary role. Current and proposed commercial scale biomass-to-ethanol facilities almost exclusively use agriculture derived residues (corn stover/wheat straw/sugarcane bagasse). Competition for biomass feedstocks for bioenergy/biofuel applications, is most likely to occur for agricultural biomass with coproduct lignin and other residues used to concomitantly produce heat and electricity on site at biofuel production facilities.
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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 | 0.002 | 0.005 |
| 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 | 0.005 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".