Urban Environmental Quality Mapping: A Perception Study On Chittagong Metropolitan City”, Kathmandu University Journal of Science, Engineering and Technology, I(IV), 1 – 14, 2007. Allis Nurdini was born in Medan, 15 th November 1977, Indonesia. She studie
Bibliographic record
Abstract
Urbanization is a global phenomenon. It is taking place at a faster pace in the less developed countries of the world like Bangladesh. The implication of such urbanization are manifested in mass poverty, gross inequality, high unemployment, crowded housing, proliferation of slums and squatters, deterioration in the environmental condition, highly inadequate supply of water, over crowding in schools and hospitals, increase in traffic jams, road accidents, crimes and social tensions. These features are the characteristics of nearly all urban centers of Bangladesh. The study has endeavored to analyze both the factual status and the perceptual pattern of the environmental quality of Chittagong Metropolitan City. The factual data have been collected from various secondary sources; while the perceptual data are based on a questionnaire survey of opinions of 492 respondents at the household level by city ward. Finally, it has been statistically justified by the use of a satisfaction index to know the degree of satisfaction of the respondents and chi-square test to examine the relationship between the income groups and degree of satisfaction. The study of perception residents f the different environmental aspects, show variation of degree of satisfaction by income groups and by groups of environmental features. The expected growth of population in Chittagong city will have adverse impact on the quality of urban environment.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".