Climate effect on rainfed wheat production in Zarfshan valley with emphasis on Ryan Panjkent
Bibliographic record
Abstract
Wheat is the main human food that is consumed directly. Recognition of climatic parameters and study of climatic needs of crop plants is one of the most important factors in the production of rainfed wheat. This study is due to the importance of climatic parameters in rainfed wheat production and also due to the potential of rainfed rainforests in Tajikistan, including Ryan Panjkent and Qa in Wadi Zarafshan. The data used in this study were collected through the Tajik Meteorological Department and the Tajik Ministry of Agriculture and the Pentecostal Agricultural Office in the field and in libraries. In the first step, the data were checked for homogeneity and uniformity. In the next step, using Lars Wg software using HadGEM2-ES series models and three scenarios of RCP26, RCP45, RCP85 in the period 2011-2050, the Lars model's ability to predict the climatic variables of Panjkent station was evaluated and then the data. The prediction was evaluated with observational data and also through Anova correlation and test between climatic parameters and production of rainfed wheat per hectare by Toronto White Climate Method. Connection results between climatic parameters and rainfed wheat production Using the analysis of variance (F) test and comparison with the table of coefficients of F showed; There is a significant relationship between rainfall in May and maximum temperature in June with wheat production and also rainfall in October, maximum temperature in November with rainfed wheat production in Panjkent station, there is no significant relationship per hectare.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".