Сравнително изпитване на биопрепарати, съдържащи хумусни субстанции, при царевични хибриди
Bibliographic record
Abstract
Field experiments on maize hybrids Kn 435, Kn 509, and Kn M625 with humus substances took place in the Maize Research Institute in Knezha in 2008-2009. The following specialty fertilizers, produced by Advanced Nutrients Itd — Canada and ROMB Ild — Bulgaria were used: Plantagra™ (PL), B-52™, MOTHER EARTH SUPER ТЕА BLOOM™ (ME), and Nirvana™ (Nir). The fertilizers were administered by foliar feeding in the 8-10 leaf phase. Them rate of introduction was 50 ml/ha for each iteration. Two additional fertilizer rates were tested оп Kn 435 — 80 ml/dа and 120 ml/da. The results show that treating with B-52 increases yield of hybrid Kn 435 by 18.47%, while treatment with PL 120 ml/da increases yield by 12.27%. Similar results were obtained using Nir (12.62%). Treatment with МЕ increases yield between 3.06% аnd 8.6%. Especially clear is the trend of increasing protein content in the range of 2.27% to 18.62% оп the tested hybrids. Best results for increasing the protein content are obtained after treatment with Nir (29.52%). The hybrid Kn 509 responds best when treatment is done with B-52 (11.64% above the control) and shows the least increase in lipids content on 625 Kn M625 (5.8% — B-52).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".