PHYTOSANITARY MONITORING OF SEEDS OF THE MAIN GRAIN CROPS
Bibliographic record
Abstract
The main prerequisite for the integrated plant protection is phytosanitary monitoring and pest prediction, which should be a system of collection, accumulation, analysis and use of phytosanitary information for the purpose of targeted and optimal implementation of plant protection measures. Under the conditions of Penza region the quality of seed material and condition of seed crops of major grain crops has been studied using modern methods of phytosanitary monitoring. It was found that the dominant pests on the crops of cereal crops are bread flea (Phyllotreta Vittula Redt.), wheat thrips (Haplothrips tritici), scarab beetle (Anisoplia austriaca beetle), various types of aphids. Among diseases there are powdery mildew, Septoria, rust, spot disease. Fields are dominated by weeds of canada thistle and chamomile, pansies, bindweed and field thistle. On average over the two years, 62160 ha have been treated with herbicides, 48900.5 ha with insecticides and 43544 ha with fungicides. The pesticide load was mainly in June and July. An average of 9,425 tonnes of spring grain crop seeds were sown in the Penza district farms in two years. Of these, 6,711.36 tonnes were treated against diseases and pests. 2866,2 tons were treated with fungicides, 441,5 tons – with insecticides, 4362,66 tons – with insectofungicides. The pesticide load (per physically treated area) was 0.76 kg/ha in these years. This is an optimum value. A load of up to 1 kg/ha is considered safe.
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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 | 0.000 |
| 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".