Sorghum Accessions for Use as Cover Crops and Biofuel Feedstocks
Bibliographic record
Abstract
Phenotypes of sorghum species (Sorghum sp.) have characteristics making them valuable summer annual cover crops and/or biofuel feedstocks for temperate climates. In field studies conducted at Urbana, IL, USA, fourteen USDA sorghum landrace accessions and three commercial sorghum accessions were evaluated for their growth habits and regrowth potential. In Canonical Discriminant Analysis (CDA) analysis, the first two canonical variates were significant and accounted for 86% of the among-accession variability. Unmown tiller number, regrowth tiller number, and regrowth biomass best discriminated between accessions in CDA and scattergrams. The accessions clustered into three subgroups. Three multi-stemmed accessions (two commercial varieties and one USDA accession) with an ability to regrow clustered away from the bulk of the USDA sorghums. Multi-stemmed accessions are useful for breeding improved summer annual cover crops that are tall, produce copious amounts of biomass, and rapidly regrow after defoliation; although propensity to lodging and poor germination of accessions will need attention. Additionally, landrace sorghum accessions in the USDA germplasm collection are useful for breeding cover crop and biofuel feedstocks, due to their great height and biomass production, although it will be necessary to select for improved regrowth potential. Crosses between USDA landraces and the commercially available multi-stemmed accessions could lead to a sorghum cover crop and biofuel plant with great biomass and height and ability to regrow following defoliation.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".