The Spatial Analysis of Insolation in Iran
Bibliographic record
Abstract
This study aims at doing a spatial analysis of insolation in Iran. The statistical time span that has been investigated is the period between 1948 and 2009. After determining insolation, the spatial analysis maps are drawn as monthly, seasonal, and an annual map for the total statistical period. After the analysis of the annual map, Iran has been divided into 5 areas regarding the amount of insolation, including: areas with the least amount of insolation, areas with little amount of insolation, areas with average amount of insolation, areas with high amount of insolation and areas with the highest amount of insolation. The results of the study show that farthest area in north west of Iran with 185 kw/m2 has the lowest amount of insolation and the farthest area in south east of Iran with 235 kw/m2 has the most amount of insolation. The results of this study are not in agreement with the results of previous studies, which have been done through experimental models. The former studies done though experimental models show that a huge part of center and some parts of north west of Iran receive the highest insolation and coasts of Caspian sea receives the lowest insolation. While the results of the present study show that southern coasts of Iran receive the highest insolation and the north west of Iran has the lowest insolation. These results are because of the low amount of cloudiness and high amount of radiation angle in the south coasts of Iran and the high amount of cloudiness and low amount of radiation angle in the north west of Iran.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| 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".