An evaluation of U.S. and Canadian lignocellulosic biomass supply and ethanol production costs
Bibliographic record
Abstract
Cellulosic biofuels are an attractive renewable fuel option. An improved understanding of lignocellulosic biomass resource availability and the expected cost of biofuel production can facilitate the development of appropriate renewable fuel policies and targets. Fifteen studies estimating lignocellulosic biomass availability for the U.S. and Canada were compared. Considerable differences in reported biomass estimates were found, largely due to different assumptions about crop yields, land area availability, and the inclusion of economic considerations. In the near-term, the largest biomass categories for the U.S. and Canada are predicted to be agricultural residues and logging residues, respectively. Near-term cellulosic ethanol production costs were reviewed and compared using a standardized methodology. The largest components of ethanol production cost are feedstock costs and capital investment and recovery costs. Further technological progress and associated cost reductions were found to be needed prior to the commercialization of biochemical conversion processes for producing ethanol.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".