The Study of The Environmental Sustainability of Rural Housing in Lorestan province, Iran
Bibliographic record
Abstract
The present study has assessed the environmental sustainability of rural housing in terms of quantitative and qualitative indicators, according to the latest information resources from the Statistical Center of Iran and the Housing Foundation of Islamic Revolution, together with analytical and technical methods.Data collection was conducted using TOPSIS method, the coefficient of dispersion, and cluster analysis.Results from the TOPSIS method, which was used for analyzing the application of environmental indicators of rural housing, show that the CLi values of this index in the cities of Dorood and Aligoodarz are 0.461376 and 0.103033, respectively.Delfan, Azna, and Khoramabad, as wealthier cities, are next in the ranking, following the city of Dorood.The CLi values obtained from environmental measures of rural housing reveals the existence of gaps and divergence between the cities of Lorestan province.Coefficient of dispersion indicators show that the ratio of housing with appropriate sewage system (wastewater) by a factor of 1.153702734 has the highest coefficient of dispersion and the ratio of housing with appropriate sewage system (toilet) with a coefficient of determination of 0.02354327 has the lowest coefficient of dispersion among the indicators.The results show that the environmental indicators in the cities such as Aligoodarz, Poldokhtar, and Koohdasht, where there are livestock activities, are in more unsustainable situation, as compared to other cities of the province.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".