Estimation of Crop Water Requirements in Irrigated Land of the Khorezm Oasis in the Context of Climate Change
Bibliographic record
Abstract
Khorezm province is located in the northwest part of Uzbekistan in the basin of one of largest water sources – the Amu Darya River - and occupies the left bank in the Amu Darya lower reaches. The area of the province is 6,100 km2. The province borders Karakalpakstan in the North, Turkmenistan in the South, and Bukhara province of Uzbekistan in the South-East. Uzbekistan is situated in the territory, where high rates of climate change are expected and observed. According to forecasts, further climate change would cause even higher air temperatures, altered precipitation patterns and severe and prolonged droughts, with consequent lowering of available water resources. More plausible scenarios for Uzbekistan suggest more than 4°С rise in average annual air temperatures by 2080. Water discharge along the Amu Darya River is expected to decrease potentially by 10-15%. Objective of given work is to analyze and assess the positive impacts of climate change through alterations of bioclimatic potential in given terrain and agromelioration parameters of crops, with consequent changes in crop water requirements. Earlier research results showed that the observed growth of thermal potential allows earlier sowing and more rapid accumulation of effective temperatures. This will shorten plant development phases, on the one hand, and, as a result, reduce water use by crops, on the other hand.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".