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Record W2117795669 · doi:10.2135/cropsci2000.4051237x

Inheritance of Resistance to Stem Rust in ‘Triumph 64’ Winter Wheat

2000· article· en· W2117795669 on OpenAlexaff
D. R. Knott

Bibliographic record

VenueCrop Science · 2000
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicWheat and Barley Genetics and Pathology
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsStem rustBiologyBackcrossingPucciniaResistance (ecology)CultivarGeneBotanyRust (programming language)Inheritance (genetic algorithm)GeneticsHorticultureAgronomyMildew

Abstract

fetched live from OpenAlex

The winter wheat ( Triticum aestivum L.) cultivar Triumph 64 has resistance to a number of races of stem rust ( Puccinia graminis Pers. f. sp. tritici Eriks. & E. Henn) and has been used as a supplementary differential in identifying stem rust races. Triumph 64 was crossed and backcrossed to a susceptible wheat, LMPG, to study the inheritance of its resistance to races MCC and LCB. Four additional backcrosses to LMPG were made to produce near‐isogenic lines (NILs) carrying the Triumph 64 genes for resistance to races MCC and LCB. Twenty‐two NILs were tested with isolates of 10 stem rust races to determine the number of genes for resistance that were present. The genetic study indicated that Triumph 64 carries two genes conditioning resistance to both races MCC and LCB, and four genes conditioning resistance only to LCB. However, the NILs produced from Triumph 64 appeared to carry at least nine Sr genes, six giving resistance to both races MCC and LCB, and only one giving resistance just to LCB. Furthermore, some of the NILs were resistant to races TMH(15B‐1) and TMH(15B‐4) to which Triumph 64 is susceptible. One possible explanation for the results is that Triumph 64 carries one or more suppressors that were lost during the backcrossing, allowing the suppressed Sr genes to be expressed. Four of the NILs were resistant to all 10 races of stem rust and should be useful in wheat breeding.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.727
Threshold uncertainty score0.437

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.020
GPT teacher head0.232
Teacher spread0.212 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2000
Admission routes1
Has abstractyes

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