THE EVALUATION OF GENOTYPE-ENVIRONMENT INTERACTION IN RED BEET VARIETIES OF VIR COLLECTION
Bibliographic record
Abstract
The article presents the results of ecological and geographical study of table beet samples of the VIR collection. The study was carried out between 2014 and 2016 in three stations located in different soil and climatic zones of the Russian Federation: in Leningrad, Moscow and Krasnodar regions. The main attention is paid to the interaction of the genotype and the environment, as the main reason for the considerable variability in the yield of table beet varieties when growing them in different ecological and geographical zones. Today the search and creation of an initial high-yielding and versatile material for breeding of adaptive beet varieties is one of the most important trends in the table beet breeding programs. The article describes the evaluation of the factors of time and place of cultivation on yield. The factors that make the greatest contribution in the formation of yield are identified. Significant variability in the yield of collection samples, depending on the cultivation zone, was noted. Samples for the intensive type of cultivation in different zones are identified. The variety of table beet for inclusion in breeding programs, as a source of adaptability and high yield is recommended. The variety of table beet «Perfected Detroid Dark Red» (Canada) is recommended for inclusion in breeding programs as a source of adaptability and high yield.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".