Screening of the mogar collection samples according to productivity and biochemical composition of biomass
Bibliographic record
Abstract
In the arid conditions of the Lower Volga region, mogar is a promising fodder crop. Therefore, the purpose of the current study was to identify promising samples for the development of new varieties that would meet the requirements of domestic agricultural producers. The paper has presented the estimation results of the mogar varieties of the VIR genetic resources collection according to morphometric parameters, productivity, and nutritional value of aboveground biomass. The study was carried out in the department of perennial and annual grasses of the FSB-SI Russian Research and Project-technological Institute of sorghum and maize “Rossorgo” in 2021–2022. The objects of study were 36 mogar varieties of various ecological and geographical origin. Based on the estimation results of the initial material of mogar, ther have been identified the promising samples for further introduction in the breeding process to increase the values of individual traits, since it is advisable to use the following samples to improve the biochemical composition of the biomass, as k-1356 (Russia), k-1745 (Bulgaria), k-1748 (Bulgaria), k-1775 (Romania) to increase the content of crude protein (> 7.50 %); k-63 (USA), k-80 (USA), k-336 (Morocco), k-1854 (Hungary), k-1877 (the USA) to increase crude fat> 3.00% ; k-1356 (Russia), k-1850 (Hungary), k-1833 (China), Asket (st) (Russia) to increase crude ash >10.00%. The varieties k-605 (China), k-1027 (Kazakhstan), Atlant (Russia) are found promising for breeding work for high yields of aboveground biomass >20.00 t/ha. The studied mogar varieties k-336, k-605, k-993, k-1027, k-1726 according to the collection of fodder units per hectare exceeded the standard variety from 1.2 to 12.9 %. The highest gross energy yield per unit area, exceeding the indicator of the standard variety, was established for the varieties k-336 (Morocco), k-605 (China), k-993 (Romania), k-1027 (Kazakhstan), k-1726 (Canada), k-1775 (Romania).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".