Grain Biodeterioration of Sorghum Converted Lines Inoculated with a Mixture of Fusarium thapsinum and Curvularia lunata
Bibliographic record
Abstract
Background and Objective: Globally, grain mold is a major hurdle affecting sorghum productivity and quality.This disease is caused by complex fungal pathogens, among them Fusarium thapsinum and Curvularia lunata are the major fungi prevalent in many sorghum growing regions.This study examined the effect of inoculating a mixture of F. thapsinum and C. lunata on 60 sorghum converted lines with five adapted inbred lines as checks.Materials and Methods: Sorghum lines and checks were evaluated in field trials at the Texas AgriLife Research Station.Plants were inoculated with a mixture of F. thapsinum and C. lunata at 50% bloom.Results: The overall result showed that SC 725 (PI 534101), SC 218 (PI 534127), SC 691 (PI 534050), SC 91 (PI 534145) and Sureno exhibited grain mold severity of 2.3 or less.This level of grain mold infection was lower than the scores exhibited by the two resistant checks RTx 2911 (2.8) and SC 719-11E (2.5).Significant negative correlation (r = -0.385,p = 0.002) between grain mold and germination indicated the impact of these two fungi infection on germination rates.The significant negative correlation detected between germination and daily maximum temperature during the evaluation period shows planting of sorghum cultivars/hybrids that mature during periods of dry moderate weather will avoid problem of grain mold infection.Conclusion: The identified four converted lines for grain mold resistance in this study is recommended to use in breeding program to introgress grain mold resistance genes into other adapted sorghum inbred lines to increase the yield and seed quality traits.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".