Changes in ethanol production potential due to species, cultivar and location on the Canadian prairie
Bibliographic record
Abstract
In recent years there has been a rapid growth in the fuel ethanol industry, increasing the need for a consistent supply of feedstock. This study was conducted to evaluate the potential of small grains in western Canada to supply feedstock to the ethanol industry. Thirty-one lines and cultivars of Canadian small grains were evaluated: eleven cultivars comprising five classes spring wheat, six cultivars of two and six row barley of feed, malting and hulless classes, eight cultivars of spring triticale and six cultivars of oat were grown at seven locations in western Canada and evaluated as feedstock for ethanol production. Starch concentrations and, for certain grains, β-glucan and pentosans were determined and used to estimate ethanol yields in L t-1 and L ha-1. On average, ethanol yield in L t-1 was wheat > triticale > barley > oat; however, for yield in L ha-1, only oat was inferior. This ranking was consistent across all locations tested. Estimates of ethanol yields indicated that certain cultivars within classes of grains were superior, such as CDC Buck, SWS 109, HY 617 and Pronghorn in the hulless barley, CWSWS, CPS-R and Triticale classes, respectively. Locations that produced the highest level of ethanol in one species tended to produce grain with the highest ethanol yields in the other species. Selection of cultivars with greater starch content, different starch quality and reduced pentosans as well as the advancements in and adoption of new fermentation technologies may lead to greater estimates of ethanol yields of small grain cereals in the future.Key words: Cereal grains, starch, pentosans, β-glucans, ethanol yield
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".