Selection-valuable barley samples of the VIR collection in terms of adaptability, productivity and grain quality
Bibliographic record
Abstract
The purpose of the research is to identify promising samples of spring barley from the VIR collection in terms of yield, parameters of the adaptive capacity of individual productivity elements and grain quality for use in creating a new breeding material adapted to the extreme conditions of Eastern Siberia. When studying the VIR collection in the conditions of Eastern Siberia for the first time, barley samples were identified according to the parameters of the adaptive ability of individual elements of productivity and yield, grain quality indicators, which are involved in crosses with local varieties. With the participation of these samples, 100 hybrid combinations were created. The studies were carried out in 2014-2017 on the experimental field of the Krasnoyarsk Research Institute of Agriculture, located in Eastern Siberia on ordinary low-power black soils, according to generally accepted methods. Agrometeorological conditions during the years of research were contrasting. It has been established that the highest grain productivity was formed by the variety Abalak (Krasnoyarsk Territory, Tyumen Region), Vaughn C.I. 11367 (k-17835, USA), Kindred (k-18048, USA), Codac (k-30874, Canada), Etienne (k-30875, Canada), Diamond (k-29192, Canada), AC Albright (k- 30601, Canada), Ubagan (k-30776, Chelyabinsk region.), Bagrets (k-30988, Sverdlovsk region), Talan (k-46502) and Tanay (Novosibirsk region), Abalak (Krasnoyarsk Territory, Tyumen region), AC Albright (k-30601, Canada), Cirstin (k- 29988, Germany), Talan (k-46502, Novosibirskaya region), Tarsky 3 (k-30719, Omsk region). In the selection of varieties of an intensive type, samples of North America, Scandinavia, Germany, Ukraine, Belarus, Kazakhstan and some regions of Russia were of interest. To increase the amount of protein in grain during crop farming, samples from Germany, Yugoslavia, Dagestan, Altai Territory and Chelyabinsk Region had an advantage. Some samples from Canada, Chelyabinsk, Novosibirsk and Altai Regions were identified based on the gross collection of protein per unit area. Samples from Canada, Finland, Sweden, as well as Novosibirsk and Omsk Regions were characterized by high grain quality.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 teacher head, 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".