Productivity and quality of seeds of collection samples of oil flax
Bibliographic record
Abstract
Abstract. The purpose of the study is to study samples of oilseed flax from the VIR collection, to identify the most promising for the conditions of the Northern Trans-Urals. Methods. Field experiments, observations and records were carried out according to VIR methods. The fat content in the seeds of flax varieties was determined using an AMB-1006M NMR analyzer. Results. As a result of studying 31 samples of the oilseed flax collection, the most productive ones were identified: k-5831 VIR 1650 (265 g/m2), k-5579 Voronezhskiy 1308/138 (261 g/m2), k-8409 Kinel’skiy 2000 (248 g/m2), k-8799 Avgust (241 g/m2) from Russia; k-6056 Bakhmal’skiy 105 (243 g/m2) from Uzbekistan; k-8606 Omega (241 g/m2) from Canada; samples with a high mass of 1000 seeds: k-8218 Micael (8.84 g) from Canada; k-8799 Avgust (8.70 g); k-8158 Sokol (8.64); k-5831 VIR 1650 (8.61 g); k-5579 Voronezhskiy 1308/138 (8.42 g) from Russia; samples with a high oil content in seeds: k-8729 Ba Ya No. 12 (50.6 %) from China; k-8799 Avgust (49.2 %) from Russia; k-6056 Bakhmal’skiy 1056 (47.6 %) from Uzbekistan; k-8610 McBeth (46.8 %) from Canada; k-7964 Rucheek (46.7 %), k-5579 Voronezhskiy 1308/138 (46.7 %), k-8158 Sokol (46.6 %), k-6986 Sibirskiy-397 (46.6 %) from Russia. Promising for use in breeding programs when creating new varieties of oilseed flax in the region can be considered samples characterized by high and elevated indicators of such important economic characteristics as yield, weight of 1000 seeds and oil content in seeds: Voronezhskiy 1308/138, Avgust, VIR 1650, Sibirskiy-397 from Russia, Oliver from France.
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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 | 0.001 |
| 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) | 0.001 | 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".