Powdery midlew is a dangerous disease of spring triticale in the far east
Bibliographic record
Abstract
The studies were carried out in 2015–2022. in order to determine the degree of damage to collection samples of spring triticale by powdery mildew in the soil and climatic conditions of the Khabarovsk Territory of the Far East. The object of research is 84 samples of various ecological and geographical origins. The weather conditions during the research period were contrasting; with the normal precipitation for April–August being 466 mm, the excess was 16…263 mm; waterlogging contributed to an increase in relative air humidity to 100 %. The soil of the experimental plot is heavy loamy meadow-brown podzolized-gley. The distribution (R) of powdery mildew in triticale crops was high (80…100 %), the intensity of development (P) reached epiphytotic values annually. Collection varieties of spring triticale were distributed according to the intensity of disease development: moderately susceptible (13 samples), susceptible (35 samples), highly susceptible (36 samples). High correlation coefficients were calculated between the amount of precipitation in the booting-earing phase and the degree of infection of plants with powdery mildew (r = 0.861…0.897), and a linear regression equation was compiled that shows the dependence of the intensity of development of the powdery mildew pathogen and the amount of precipitation in the second ten days of June. Thus, spring triticale varieties were identified that have average susceptibility to powdery mildew pathogens (P = 30…40 %): Amore, Saur, Prag 409, Dagvo (Russia), Lana, Lotos (Belarus), Zgurivskiy, Oberig Kharkovskiy (Ukraine), 70 HN 458 (Canada), Je 57 (USA), Anoas 5, MX 51 (Mexico), Tleridal (Switzerland).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".