A review of U.S. and Canadian biomass supply studies
Bibliographic record
Abstract
An improved understanding of lignocellulosic biomass availability is needed to support proposed expansion in biofuel production. Fifteen studies that estimate availability of lignocellulosic biomass quantities in in the U.S. and/or Canada are reviewed. Sources of differences in study methods and assumptions and resulting biomass quantities are elucidated. We differentiate between inventory studies, in which quantities of biomass potentially available are estimated without rigorous consideration of the costs of supply, versus economic studies, which take into consideration various opportunity costs and competition. The U.S. economic studies, which included reasonably comprehensive sets of biomass categories, estimate annual biomass availability to range from 6 million to 577 million dry metric tonnes (dry t), depending on offered price, while estimates from inventory studies range from 190 million to 3849 million dry t. The Canadian inventory studies, which included reasonably comprehensive sets of biomass categories, estimate availability to range from 64 million green t to 561 million dry t. The largest biomass categories for the U.S. are energy crops and agricultural residues, while for Canada they are expected to be energy crops and logging residues. The significant differences in study estimates are due in large part to the number of biomass categories included, whether economic considerations are incorporated, assumptions about energy crop yields and land areas, and level of optimism of assumptions of the study.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.027 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".