Potential utilization of native prairie grasses from western Canada as ethanol feedstock
Bibliographic record
Abstract
The utilization of native grass species for potential biomass feedstocks of the emerging ethanol industry requires more information about their cellulose and hemicellulose concentration. Ten native species were grown at seven sites across the prairie region of western Canada for two to four growing seasons. Northern wheatgrass, Elymus lanceolatus, produced high concentrations of cellulose (363 g kg-1) but low concentrations of hemicellulose (266 g kg-1). Green needlegrass, Nasella viridula, produced high concentrations of both constituents (351 and 307 g kg-1). Four warm-season grasses, big bluestem, Andropogon gerardii, little bluestem, Schizachyrium scoparium, prairie sandreed, Calamovilfa longifolia, and switchgrass, Panicum virgatum, had 346, 342, 340 and 338 g kg-1, respectively, concentrations of cellulose and also exhibited a positive response to temperature that resulted in increased hemicellulose concentration. Accumulated thermal time (degree day base 10°C) was correlated to hemicellulose concentrations in the warm-season grasses but not for cool-season grasses. Holocellulose (cellulose + hemicellulose) concentration differences varied among site-years but warm-season grasses were more stable in hollocellulose concentration than cool-season grasses. Key words: Biomass, native grasses, cellulose, hemicellulose, biofuel
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".