Effects of Continuous Rainy Weather on Pea Pod Set Rate and Preventive Measures
Bibliographic record
Abstract
Pea is one of the important legume crops in my country, but its pod setting rate is easily affected by environmental conditions. In recent years, continuous rainy weather has occurred frequently in some planting areas, resulting in insufficient light, soil moisture and high incidence of diseases, which have led to the obstruction of pea flowering and pod setting and a decrease in yield. This study focuses on the physiological mechanism of pea pod setting, systematically analyzes the specific effects of continuous rainy weather on the pea pod setting rate, including photosynthetic restriction, root inhibition and pollination and fertilization barriers, compares the response differences of different pea varieties to continuous rain, explores the characteristics of highly sensitive and shade-tolerant varieties and their pod setting rate under rainy conditions, and proposes targeted prevention and control strategies and field management measures on this basis. Through case analysis in Yunnan, Gansu, Guizhou and other places, local experience is summarized and the effectiveness of the above measures is verified. This study provides a scientific basis for coping with the decline in pea pod setting rate under abnormal climatic conditions, and hopes to enhance the risk resistance of pea production.
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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) | 0.001 | 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".