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Fusarium head blight of winter triticale varieties in the Forest-Steppe zone of Ukraine

2016· article· en· W2560575581 on OpenAlexaboutno aff
М. М. Ключевич

Bibliographic record

VenuePlant varieties studying and protection · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMycotoxins in Agriculture and Food
Canadian institutionsnot available
Fundersnot available
KeywordsTriticaleFusariumBiologyBlightAgronomyRoot rotHybridResistance (ecology)Horticulture

Abstract

fetched live from OpenAlex

Purpose. Studying variety samples of winter triticale of various ecological and geographical origin for revealing polymorphism of the culture for its susceptibility to pathogenic complex of Fusarium head blight and defining high-yielding and resistant to diseases varieties that later can be put into the production and breeding process. Methods. Field experiments, statistical evaluation. Results. It was defined that the development of Fusarium head blight in different variety samples of winter triticale depends on the hydrothermal conditions and genotype of the host-plant. Resistance of varieties and hybrids to the disease was partial, and no immune samples were found among the analyzed ones. The following varieties proved to be tolerant to Fusarium head blight: ‘Granat’, ‘Zorro’, ‘Obrii Myronivskyi’. The positive correlation between the development of Fusarium head blight and root rot of winter triticale was found. It was determined that in the pathogenic complex of Fusarium head blight the amount of the following pathogens is increasing: Fusarium sporotrichioides, F. аvenaceum and F. poae. Conclusions. The leading varieties that combine high yields and resistance to Fusarium head blight are as follows: ‘Obrii Myronivskyi’, ‘ADM 8’, ‘Yuvileine Volynske’, ‘Yukon’, ‘Zorro’, ‘Tsekad 90’, ‘Zerniatko’, ‘Legion’ and ‘Rarytet’. These varieties should be involved in the selection process to breed the hybrids with the augmented resistance to the disease and high yields.

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 score0.804
Threshold uncertainty score0.117

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.034
GPT teacher head0.207
Teacher spread0.173 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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