Regionalization of the Climatic Areas of Qazvin Province Using Multivariate Statistical Methods
Bibliographic record
Abstract
The logical explanation about the spatial organization and usage of local capabilities and application of basic strategic to obviate complications is required to identifying the environment and using modern techniques to achieve this goal. Climatic regionalization or knowing different climatic regions is necessary for planning and territory sustainable development. The aim of this research is to recognize the most influential climatic elements affecting the climate of Qazvin province, and the spatial separation of climatic regions by using multivariate statistical methods. To this end, mean data of 28 climatic elements in 20 internal synoptic climatology stations both inside and adjacent to the borders of Qazvin Province were gathered and exploited. The correlated matrices of standardized data of the 28 climatic elements were analyzed using factor analysis in 623 spatial pixels, in the province of Qazvin. This analysis on the correlated matrices of standardized data showed that with 9 principal components, more than 99.21% of the spatial changes of the regional elements in the province can be described. At the same time, the temperature elements, humidity elements and rainfalls have in the central and eastern and north and south evident in the province. Applying agglomerative hierarchical cluster analysis in
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| 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".