Integrated protection of winter wheat from weeds
Bibliographic record
Abstract
The low resistance of plants to weeds and the high potential contamination of the soil by the most harmful weeds is the most important reason for the shortage of the crop yield. The subject of research was the development of integrated weed control measures based on the analysis of the structure of the weed component in winter wheat. The study has shown that infestation with juvenile and offset weeds of is predominant in the studied area. Due to the occurrence of weeds, it was found that the largest proportion of juvenile weeds in winter wheat sowings is represented by penny cress (Thlaspi), and the perennial weeds are represented by Canadian thistle (Cirsium arvense). According to the study results of the agrophytocenosis, such weeds as Canadian thistle (Cirsium arvense) and goosefoot (frost blite) are the most harmful in the upper layer during winter wheat earing period. During this period in the middle and lower layers there were such weeds specific for winter as penny cress (Thlaspi), flixweed (herb-Sophia) and henbit dead-nettle (common henbit). There has been proposed a combination of preventive, phytocenotic, mechanical and chemical measures for weed control to protect winter wheat crops against weeds of various biological groups, including those specialized for the very crops. It has been established that with a mixed type of weed infestation and 32.2 pcs/m 2 of weeds in winter wheat crops, there is an economic threshold of harmfulness, which determines the necessity to apply integrated weed control measures. The introduction of the developed measures to control weeds provides an increase in profitability level of winter wheat cultivation on 15...20%.
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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) | 0.002 | 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".