Relationship among seed germination and other characters associated with fusarium grain mould disease in sorghum (<i>Sorghum bicolor</i>L. Moench) using path coefficient analysis
Bibliographic record
Abstract
Fusarium grain mould disease (FGMD) is a part of the grain mould disease complex and is caused by Fusarium species which are capable of infecting spikelet tissues at anthesis or immature grain up to physiological maturity. The disease causes severe losses in quality, viability and germination of sorghum seed. To determine the effect of FGMD associated characters (panicle grain mould score (PGS), seed rot, amount of Fusarium, and non-Fusarium infected seed and seed weight) on seed germination and to find the inter-relationship among these characters, replicated field trials were conducted with 36 sorghum recombinant inbred lines (RILs) during 2009 and 2010 at Hyderabad, India. PGS showed a strong positive relationship with seed rot and seedborne Fusarium (P < 0.01). Seed rot showed a significant positive relationship with seedborne Fusarium and a negative relationship with seed weight and germination (P < 0.01). Frequency of seedborne Fusarium on mould-infected sorghum seed had a strong negative correlation with that of non-Fusarium infection (P < 0.01), suggesting interactions between them in causation of grain mould in sorghum. Path coefficient analysis for seed germination revealed that seed rot (−0.43) and PGS (−0.28) had a maximum direct effect, accompanied with less interference by other factors, on seed germination. Seed rot has emerged as the most important parameter for determining seed germination in moulded sorghum grains. Few promising RILs that produced minimum premature seed rot were identified. The RIL numbers 144, 156 and 159 were superior to controls for many FGMD associated characters and could be useful sources for improvement of FGMD resistance.
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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 | 0.001 | 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 | 0.000 | 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".