Choosing parental pairs for development initial material of alsike clover
Bibliographic record
Abstract
The article presents the results of a study in 2021 of 13 tetraploid selective varieties of alsike clover (Trifolium hybridum L.) from Norway, Germany, Sweden, Denmark, Canada, Latvia, Belarus and Russia in terms of the productivity of air-dry matter of the standing crop and seed yield. The observations were carried out in a greenhouse experiment set up in the greenhouse complex of the Federal Williams Research Center of Forage Production & Agroecology. The purpose of the research is to choose for crossing and further breeding work the most productive tetraploid clover genotypes from different geographical regions in terms of standing crop and seed yield. The criterion for selecting promising selective numbers for crossing was the deviation of the standing crop yield and (or) seed yield by more than 3 standard deviations (σ) from the corresponding arithmetic mean in the experiment. For experimental data on the yield of air-dry matter of the standing crop, a normal distribution was characteristic, and for seed yield, an exponential distribution. This made it possible to use the critical value of 3σ in making breeding decisions. The average productivity of air-dry matter of the standing crop is 47 g/vessel, σ=16 g/vessel. The average seed productivity is 1.1 g/vessel, and σ=0.9 g/vessel. According to the value of air-dry matter of the standing crop of 102 g/vessel, selective number 56 (cv. Tetraploid from the Republic of Belarus) was identified, and according to seed yield of 4.2 g/vessel, selective number 42 (cv. Alpo from Norway). They belonged to the productivity of Novator variety 213% and 233%, respectively. Selective numbers 56 and 42 are promising for crossing and studying the ability of offspring to combine high fodder and seed productivity.
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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 | 0.001 | 0.000 |
| 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) | 0.004 | 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".