Yield and fat content in oil flax seeds under the conditions of the Northern Trans-Urals
Bibliographic record
Abstract
Oil flax is a valuable agricultural crop that is widely used in industry. Technical oil and cheap vegetable protein for livestock production are obtained from it, which makes it a valuable feed crop for the production of oilcake and presscake. The purpose of the work was to study collection samples of oil fl ax in the forest-steppe zone of the Northern Trans-Urals, to identify the best in terms of yield and fat content. It was revealed as a result of the research that some samples of oilseed fl ax from the world collection of All-Russian Institute of Plant Growing have high adaptability to the conditions of the forest-steppe zone of the Northern Trans-Urals. These samples are characterized by high yield, as well as a high amount of oil in the seeds. Thus, the use of these samples in breeding work will make it possible to create new varieties of oil fl ax that meet modern production requirements. As a result of the research, Russian varieties were identifi ed that were distinguished by high productivity such as Voronezhsky 1308/138, VIR 1650, Sibirsky 397, and August. Some varieties of imported selection such as Micael (France), Omega amd Prairie Blue (Canada), Chibik (Chibis, Ukraine) and BaYaNo 7 (China), their yield varied at the level of 210–247 g/m2. The highest fat content in seeds was in the following varieties: BaYaNo 12 and BaYaNo 7 (China) – 50,7 and 47,5 %, August (Russia) – 47,5 %, Bakhmalsky 1056 (Uzbekistan) – 47,6 %. The selected varieties are recommended for use in practical selection and production on the soils of the Northern Trans-Urals region.
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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 | 0.000 | 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) | 0.001 | 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".