Housing Growth Potential Based Fuzzy Simulation Zonal Ranking: A Case Study Of Indian Metropolitan
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Population explosion, industrial development and urbanization are closely interknitted and as such forms a global phenomenon.India, being a third world country has been undergoing through this process in a significant way. The Class-I cities and the Metropolitan cities in particular are under rapid urbanization process. Therefore, these centers are experiencing tremendous pressure on resources and urban infrastructure. In Indian Urban system, the interplay of heterogeneous socio-economic groups, infrastructure system components and resources constraints are subjected to urbanization ambivalence. In this process, urban housing sector has been worst affected with clear cleavage between supply and demand, resulting in huge housing shortage. Therefore, the urgency to reduce the gap between housing requirement and supply can not be underscored. The Indian metropolitan under study is an evolved city. It has recorded highest growth rate in the region as well as state. It has experienced one of the fastest growth rates even at the national level. With the rapid growth the city boundaries are increased. For the sake of administration the area under study is divided in seven zones, but for the purpose of technical study the metropolitan area is divided into twenty various study zones. The delineation was based on population distribution and aerial features. Zonal housing growth potential can be defined as ability of a zone to attract the house demanding population to satisfy their actual demand in the varying affordable manner. The housing Growth Potential varies from zone to zone. It is a complex phenomenon and depends upon various varying parameters. On the basis of various pilot housing studies and correlation findings few major factors were found highly influencing.Therefore, the parameters involved are pertaining to land, where the land value plays the major role. Secondly road features like accessibility and road area network are significant. Thirdly the utility services provided to the people are also important. Apart from this, the ongoing Housing activities and the population density are also playing the major role. The Urban area is dynamic in nature. The urban zones have built in potential to attract the people and thus influence the entire city also. Very surprisingly it can be stated that the zonal potential for housing growth varies with time and policies of the planners. It is a unique exhibition of manmade and natural interplay. 1 Asst. Professor, Civil Engineering Dept., S.V. National Institute of Tech., Surat, India Phones +91 261 222337174, + 91 9427148108, macwan112@yahoo.co.in 2 Professor Emeritus, Civil Engineering Dept., S.V. National Institute of Tech., Surat, India. Phone + 91261 2211967 June 14-16, 2006 Montreal, Canada Joint International Conference on Computing and Decision Making in Civil and Building Engineering
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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 | 0.001 | 0.000 |
| 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 it