Identification and Zoning of Urban slums by Using of GIS. The Case Study of Pars Abad city
Bibliographic record
Abstract
Disorganized urban context one of the items in all the country's cities, each of which fits the human condition and is in need of the intervention and its natural. Since the abnormal tissue formation in most cities, core cities include unknown disrupt the formation of the spatial relationship and unity between the central, central and peripheral elderly. In this study PARSABAD selected as a case study and to prioritize and identify abnormal tissue was. Considering the purpose of the application and the nature and methods of descriptive research - analysis using SPSS software is the summary of the data examined, the more the use of model ANP (network analysis) and using indicators of so-cial, economic, physical, environmental indicators to assess and compare the 7 quarters and the third quarter of disorders that greater priority, priority were analyzed and evaluated. The locations are as follows Creek neighborhood Torab Abedian neighborhood, a neighborhood slaughterhouse. It should be noted that at the end of using layers of each of the criteria used to evaluate the kernel community and also map in ARC GIS software in each of the criteria analyzed And in the EXCEL software to plot each criterion were summarized and determined that the areas under consideration zire nahre torab neighborhood in turmoil maximum. The necessary planning more you told.
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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 | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".