STUDY OF SOURCE MATERIAL FOR EDAPHIC SELECTION OF ALFALFA
Bibliographic record
Abstract
The study of collectible samples of alfalfa of different ecological and geographical origin on the harvest of dry matter and seeds against the background of increased acidity of soil solution (pH 5.20-5.53) made it possible to highlight the promising beyond these indicators samples that can be used in further breeding work. Comparison of the height of alfalfa plants in the first slope and feed productivity in general for 2019 shows that there is no direct correlation between these values. For example, the Galaxie variety, which was itself visopory and exceeded the standard by 12cm, was only in fourth place for the dry crop, and the Olga and Media varieties, which exceeded the standard by 9cm, were only eighth and seventh in the dry material. However, for the selection of varieties that combine these two traits, donors may be samples that behind the harvest of dry matter and the height of plants for three years of research reliably exceeded the standard: Banat, Vavilovka (Rodnichok), Feraks 58, Galaxie and Ferax 28. Not all samples form the maximum of green mass in the first slope, so there is no definite pattern between the height of plants in the first slope and the collection of dry matter for the entire growing season. The highest yield of dry matter on average for three years of research was obtained from varieties Vavilovka (Rodnichok) (Ukraine) (1.42 kg/m2), Banat (Serbia) (1.36 kg/m2) and Posevnaya 3022 (Uzbekistan) (1.22 kg/m2) at the harvest of the Sinyuha-standard grade 1.08 kg/m2. The best in seed productivity were samples of Jidrune (Lithuania) (31.9 g/m2), Feraks 58 (Canada) (29.9 g/m2), Tibet (Kazakhstan) (28.5 g/m2), Radoslawa (Ukraine) (28.2 g/m2), Kishvardi 27 (Hung.) (27.7 g/m2), Olga (26.5 g/m2) and Vavilovka (Rodnichok) (Ukraine) (26.2 g/m2) with a yield of standart of 23.5 g/m2. Particular attention is paid to the variety Ferax 58 (Canada), which for all the years of research reliably exceeded the standard for seed harvest and was at the level of the standard or exceeded it in the yield of dry matter. Further breeding work will use samples that reliably exceeded the standard for the average of three years of research for the collection of dry matter and the seed crop respectively: Ferax 58 (10% and 27%), Radoslawa (7% and 20%), Olga (11% and 13%), In addition, these samples exceeded the standard for plant sisoin in the first slope in 2019 by 31%, 11%, 8% and 55%, respectively. Key words: alfalfa sowing, selection, sample, variety, dry matter crop, seed harvest.
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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 | 0.000 |
| 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".