Sources and Genetics of Resistance to Soybean Rust Phakopsora pachyrhizi (H. Sydow & Sydow) in Nigeria
Bibliographic record
Abstract
In searching for resistance genes to soybean rust (Phakopsora pachyrhizi (H. Sydow & Sydow), twenty eight soybean genotypes obtained from the International Institute for Tropical Agriculture (IITA) Ibadan were screened on rust infested field for resistance to rust. These genotypes were planted at the National Cereals Research Institute (NCRI), Yandev station in Benue State, Nigeria during 2007 and 2008 cropping seasons. Soybean rust is known to be endemic to the Yandev station. Field observations revealed significant variation among the soybean genotypes to rust. Seven soybean genotypes were identified to be resistant to rust which were TGx1987-62F, TGx1935-3F, TGx1951-3F, TGx1936-2F, TGx1987-10F, TGx1972-1F and TGx1949-8F. Genetic analysis of the parental materials after hybridization for the mode of inheritance indicated that rust resistance in soybean was monogenically controlled by dominant genes. The relationship among the resistance genes was established through allelic testing which indicated that genes in TGx1972-1F and TGx1987-10F are allelic and independent of the dominant resistance genes of TGx1951-3F, TGx1936-2F, TGx1987-62F and TGx1935- 3F which are also allelic. The results revealed that dominant alleles at three loci conditioned resistance to soybean rust races found in Nigeria and the tentative symbols formulated for the three loci controlling resistance to rust in soybean were Rsbr1, Rsbr2 and Rsbr3 (Resistance to soybean rust )
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".