The database of genetic resources in the VIR winter rye collection as a means of classification of genetic diversity, analysis of the collection history and effective study and preservation
Bibliographic record
Abstract
Winter rye is the second bread and the most valuable forage crop. Rye is cultivated primarily in Russia, Germany, Poland, Belarus, Ukraine, Scandinavia, China, Canada and the United States. The acreage allocated for the cultivation of rye in the world is declining (from 15.4 million ha in 1986 to 4.4 million ha in 2016). In all areas of cultivation rye has earned a reputation as the most adapted to the climatic conditions of the insurance culture of low economic risk. For the expansion of crops of rye and an increase in the gross yield of grain, it is necessary to create new varieties of rye. Currently, 94 gene banks in the world store 22,200 samples of winter and spring rye. Gene banks are located around the world; the largest of them – the N.I. Vavilov All-Russian Institute of Plant Genetic Resources (3260 samples) – is located in Russia. The collection of the world’s genetic resources of rye, concentrated in storage and propagated in the fields, contains varieties, donors, populations and lines of cultural, weed-field, wild, winter and spring rye. The collection is being constantly updated and replenished with new samples, the system of reliable storage and maintenance of the high viability of seeds is being improved, the sources of traits with value for breeding are being identified and studied, and donors are being created. Scientific, breeding and educational institutions are being supplied with source material. An electronic passport documentation system of the collection is being developed and integrated into the international system of genetic banks. In this paper, a brief analysis and characterization of the VIR rye collection is given. The history of the pre-selection study and the stages of the creation and use of donors for various problems of selection are reviewed, a passport database on winter and spring rye has been created.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.011 | 0.014 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.032 |
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".