ASSESSMENT OF SOFT WINTER WHEAT VARIETIES ADAPTABILITY TO THE ECOLOGICAL CONDITIONS OF THE CENTRAL CAUCASUS FOOTHILL ZONE
Bibliographic record
Abstract
The objective of the research was to study the environmental response of soft winter wheat accessions to the natural conditions of the foothill zone of the Central Caucasus In 2022–2024, 15 varieties of winter soft wheat were studied in the foothill zone of the Central Caucasus, including samples from the collection of the All-Russian Institute of Plant Genetic Resources named after N.I. Vavilov (VIR). Field trials were conducted in accordance with the State Variety Testing methodology. Various indices were used to assess drought resistance: spike linear density index (SD) — the number of grains in an ear/ear length; Canadian index (Ki) — grain weight per ear/ear length; plant productivity index (PPI) — the ratio of the product of the number of grains in an ear by the weight of the grain in an ear to the length of the ear; yield stability index (YSI). Drought resistance indices were calculated for all varieties based on yield data in the driest (2024) and more favorable (2022) years. The total score of each sample ranks for all indices was also calculated. Under drought conditions, soft winter wheat forms were selected that consistently provide all the main elements of the crop structure. The most productive and resistant to abiotic environmental factors varieties of winter soft wheat include 2 samples from Iraq № T1 and № T3 (V. graecum and V. ferrugineum), the varieties Arap and Naz (V. erytrhospermum) from Kazakhstan, Chornobrova (V. uralicum) from Ukraine and Livius (V. erytrhospermum) from Austria. Resistant to fusarium head blight include: Naz (V. barbarossa), Su-Mai 3 (V. ferrugineum), Livius and Arap (V. erythrospermum), № T1, T3, T17 from Iraq (V. ferrugineum and V. graecum), K-21923 (V. delfii). These samples, selected by a set of characteristics, are promising for use in breeding for drought resistance in the conditions of the foothill zone of the Central Caucasus. The proposed index system allows us to evaluate various aspects of drought resistance and adaptability of variety samples.
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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 | 0.001 |
| 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".