Classification of soybean varieties from the VIR collection by economically valuable traits using the clustering method
Bibliographic record
Abstract
The studies were carry out in 2021–2023 at the Omsk Agrarian Scientific Center in the southern forest-steppe of the Omsk Region. This article presents the results of a comprehensive assessment of 63 collection samples of soybeans from the All-Russian Insti-tute of Plant Genetic Resources named after N.I. Vavilov (VIR) of various ecological and geographical origins: from Russia (30 pcs.), Ukraine (7 pcs.), Po-land (7 pcs.), the Republic of Belarus (6 pcs.), Sweden (4 pcs.), Canada (3 pcs.). We used the VIR methodology (2018) for field tests, accounts and observations. The statistical data processing was carried out according to the B.A. Dospekhov's manual by (1985) using the Microsoft Excel application package. The clustering data was performed by the un-weighted pair group method with arithmetic mean (UPGMA) using standard GNU Octave functions for 14 economically valuable features: plant height and attachment of lower beans, the number of productive branches, nodes, beans and seeds per plant; seed weight per plant and weight of 1000 seeds; proportion of protein and fat in seeds; field germination, plant survival; seed yield per 1 m2; duration of the growing season. We identified six clusters differing by degree of severity of the economically valuable parameters. The most numerous were the first three clusters, which included 15, 22, and 16 samples, respectively. Varieties from the third cluster are sources of early maturity (96 days), but they are the shortest-stemmed (59.3 cm) with insufficiently high attachment of the lower beans (9.1 cm) and low-yielding (204.2 g/m2). Samples from the smallest fourth and fifth clusters, two and three pieces, respectively, are of the greatest interest for breeding to increase yield: Persona and Garmoniya (All-Russian Research Insti-tute of Soybeans, Russia), Anthracite (Ukraine). With yields ranging from 352 to 361 g/m2 (significantly higher than the standard variety Sibiriada), they had optimal stem height (more than 82 cm) and attach-ment height of the lower beans (11–12 cm).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".