A Canadian Ethanol Feedstock Study to Benchmark the Relative Performance of Triticale: I. Agronomics
Bibliographic record
Abstract
A need has been identified for alternative crop(s) with high grain yield, low grain protein concentration, and high starch for the ethanol industry. The objective of this study was to benchmark the relative performance of triticale (×Triticosecale ssp.) to wheat (Triticum aestivum L.) classes currently utilized for ethanol production. Sixteen cultivars—three triticale, four Canada prairie spring (CPS) wheat, three Canada western soft white spring wheat (CWSWS), two Canada western red spring (CWRS) wheat, and four Canada western general purpose (CWGP) candidate cultivars—were grown at 36 locations across western Canada from 2006 to 2009. The performance of these cereal classes can generally be summarized as triticale = Hoffman (CWGP) = CWSWS > CPS white > CPS red > CWRS for most variables. The triticale and white wheats produced 12 and 13% more grain, respectively, than the hard red spring wheats. Among the triticales, AC Ultima’s and Pronghorn’s yield potential were most notable because they exceeded the CWRS cultivars AC Barrie and AC Superb by an average of 32% and the CPS red cultivars 5700PR and AC Crystal by 18%. The triticales and Hoffman matured later than most other cultivars. Pronghorn consistently displayed low levels of fusarium head blight (FHB), Septoria nodorum blotch, and powdery mildew, but elevated ergot levels were observed for all triticales. We conclude that triticale would be superior to CPS and CWRS wheat and similar to CWSWS in many agronomic traits desired by ethanol fermentation plants and is superior for biomass production.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| 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".