Agroecological study of garden carrot cultivars from collection of Vavilov institute
Bibliographic record
Abstract
Carrots are one of the most important root crops in the world. Due to such qualities as plasticity and relative non-wholesome cultivation, carrots are cultivated in most countries of the world. Carrot roots are a valuable source of vitamins A, B, B2, B6, B12, C, RR, E, R. Agroecological conditions of the region allow to cultivate carrots in the open ground. Astrakhan region is not yet characterized by high production rates, as its cultivation can be done only under irrigation. The article considers the influence of agroecological conditions on crop yield and adaptability of garden carrots cultivars in the arid zone of the Caspian region. Experiments on studying the carrots cultivars was carried out on the fields of Precaspian Agrarian Federal Scientific Center of the RAS in 2017-2019. The purpose of the research was to study garden carrots cultivars from collection of plant genetic resources of Vavilov Institute for the selection of high-productive and more adapted samples. The object of research was 17 types of carrots from the world Vavilov collection. Based on three-year studies on yield, we can distinguish the following cultivars: Berlanda F1 (Netherlands), Nantese (Italy) and Imperator Type 9-11 (USA) with yield of 68.4 to 75.2 t/ha. The coefficient adaptability was higher than 1, in the varieties Berlanda F1 (Netherlands), Nantese (Italy), F1 Eagle (Canada), Imperator Type 9-11 (USA), Wav-88 (Germany), Surazhevskaya-1 (Russia). They have ability to adapt to difficult growing conditions and produce consistently high yields. The samples selected can be used in the future to create new cultivars and hybrids for conditions of the Caspian region.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".