Bibliographic record
Abstract
Spring wheat is a strategically significant agricultural crop all over the world. Increasing yields of the crop need increasing use of the mineral fertilizers and chemical fungicides – using which becomes less and less popular. They are being substituted with various bioproducts being developed all over the world, including Russia. All-Russian Research Institute of Reclaimed Lands (VNIIMZ) has created a novel bioproduct – LBP – featuring physiologically significant amounts of growth factors and nutritive elements favorable for the plants. This work evaluates an LBP effect on spring wheat, Irgina sort, when using LPB as a supplementary fertilizer with a mineral fertilizer as a basic one. The research was carried out in microplot experiments at a VNIIMZ’s test site, Tver Region, Russian Federation, in 2009-2010. Among all options studied, a 0.1 l/sq.m LBP dose (added by spraying on bushing-out and earing plants) proved to be the most effective. That option yielded 16.31 metric centners/hectare, which is 27.3% higher than the same without LBP is. A grain quality analysis showed the following nutritive value rise compared to references: cellulose, oil and calcium (CaO) increased by 10…12%, 9…10%, and 10…12%, respectively. Soils of the plants treated with LBP generally contained more nitrogen compounds, less amylolytic microorganisms (competing for nitrogen) and Fusarium wilt ones - which totally provided better conditions for the spring wheat growth.
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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 | 0.000 | 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".