Estimation of the initial material of spring barley in the Rostov region
Bibliographic record
Abstract
The current paper has presented the study results of the collection samples of spring barley. The purpose of the study was a comprehensive research of the collection of spring barley varieties and lines of various ecological and geographical origin in order to identify the most valuable economic and biological forms for targeted use in breeding programs of the department of barley breeding and seed production in the Federal State Budgetary Scientific Institution Agricultural Research Center “Donskoy”. The study was carried out on the experimental plots of the FSBSI “ARC “Donskoy” in 2017–2019. The objects of the study were 85 spring barley samples. The collection seed-plot was formed from the best varieties of breeding institutions in various regions, most of which were the varieties of domestic breeding (FSBSI FRC All-Russian Institute of genetic resources of plants named after N.I. Vavilov, FSBSI “ARC “Donskoy, FSBSI “National Center of grain named after P.P. Lukyanenko”, FSBSI North-Caucasus FSAC, etc.), as well as the varieties of the European Union (England, Germany, France, Czech Republic, Denmark, Latvia), Canada, Belarus, Ukraine. The sowings were carried out in a row method with a row spacing of 15 cm with the Wintersteiger Plotseed seeder; the plots were seven-row, with an area of 10 m2 . The seeding rate was 450 germinated seeds per 1 m² without repetitions. The standard variety ‘Ratnik’ was sown every 20 numbers in the seed-plot. The forecrop was sunflower. There were made phenological observations, estimated varieties’ resistance to lodging and diseases, assessed productivity and conducted structural analysis of plants in points according to the estimation system of the main economically valuable traits given in the Methodological recommendations for studying the world barley and oats collection (2012). As a result, there were identified the following varieties with a complex of economically valuable traits, as ‘Kazer’, ‘Azov’, ‘Tan 1’, ‘Divny’, ‘Chelyabinsky 99’, ‘Khadzhibey’, ‘Raushan’, ‘Agat’, ‘Suzdalets’, ‘Bagrets’, ‘Rus’, ‘Elf’, ‘Rakhat’ (Russia); ‘Nord 071111’, ‘Obolon’, ‘Odessa 22’, ‘Donetsk 14’, ‘Donetsk 15’ (Ukraine); ‘Perun’, ‘Prestige’ (Czech Republic); ‘Viking’, ‘Philadelphie’ (Germany). The identified varieties are going to be used in further breeding programs of the FSBSI “ARC “Donskoy”.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".