Analyses of sorghum [<i>Sorghum bicolor</i> (L.) Moench] lines and hybrids in response to early-season planting and cool conditions
Bibliographic record
Abstract
Kapanigowda, M., H., Perumal, R., Aiken, R. M., Herald, T. J., Bean, S. R. and Little, C. R. 2013. Analyses of sorghum [ Sorghum bicolor (L.) Moench] lines and hybrids in response to early-season planting and cool conditions. Can. J. Plant Sci. 93: 773–784. Early-season cold tolerance in sorghum contributes to emergence, seedling establishment, and early vegetative growth, and reduces damping-off diseases under chilling conditions. The objectives of this study were to identify cold-tolerant sources and to evaluate and optimize rapid screening techniques under a controlled environment. Field studies involving 48 genotypes, representing phases of the hybrid development process (landraces, elite and advanced breeding lines, recombinant inbred lines (RILs) and hybrids were conducted with early and normal planting dates in 2011 at Hays and Colby, Kansas. Studies under controlled environments were conducted at both locations using 18 genotypes that differ for emergence index (EI) and 30 d after emergence (DAE) shoot biomass based on field studies during 2011. Significant differences among the genotypes were recorded for all seedling traits (emergence percentage, EI, shoot biomass, plant height, and leaf number measured 30 DAE), and agronomic traits (days to 50% flowering, panicle exsertion, panicle length, and plant height at maturity). Eight advanced breeding lines: ARCH10731, ARCH10732, ARCH10736, ARCH10737, ARCH10738, ARCH10739, ARCH10744 and ARCH10749 and one RIL (RTx430/SQR-2) were found to be potential sources of cold tolerance with early EI, higher biomass and relatively early flowering. These genotypes are free from tannin, which helps to increase the feed grain efficiency of livestock, and hence were selected for test hybrid evaluation to assess fertility status, combining ability and yield performance. Significant correlation was observed between EI and biomass during early planting, which indicated that late-emerging genotypes produced greater biomass (30 DAE) compared with early-emerged genotypes. Significant correlation between growth chamber and field study for EI offers a potential and fast preliminary high-throughput screening technique for identification of cold-tolerant sorghum.
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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 | 0.000 |
| 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) | 0.001 | 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".