The Use of Remote Sensing and Geographical Information Systems for the Forecasting of Wheat Yield by Ostan in Iran
Bibliographic record
Abstract
Low-cost, accurate yield prediction is an important tool for assessing the state of the world's grain markets. Governments and organizations around the world use geomatics technologies, such as remote sensing and geographical information systems (GIS), as a means of providing practical, real-time analysis capabilities, which can be utilized in many ways, including yield forecast. This paper outlines a procedure to forecast wheat yields at the end of a growing season. Remote sensing data such as SPOT imagery were used, in combination with IDRISI and ArcView software, for yield forecasting. In this study the forecasting of wheat yields was done over two growing seasons; namely, a short growing season and a long growing season. Forecasts were made for 9 Iranian Ostans (provinces) with short growing seasons and for 19 (the remaining provinces) with long seasons. Multiple regression analysis was used for forecasting wheat yield. The dependent variable for this analysis was the wheat yield for each province, fo...
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".