Study of Urban Heat Island of Karachi by Using Finite Volume Mesoscale Model
Bibliographic record
Abstract
Karachi is the largest and most populous metropolitan city of Pakistan and the main seaport and financial centre of the country. Rapid urbanization during last 40 to 50 years caused to accelerate its population to 23.7 million. The objective of this work was to study the impact of urbanization on development of urban heat island (UHI) of Karachi city. The study was conducted by using Finite Volume Mesoscale Model (FVM) in which topography from GTOPO30, land-use from GLC 2000 and Meteorological data from NCEP were used as an input to run the simulation. The simulations were run for three days starting at 00:00 (GMT) on 19th day of April and ending at 00:00 (GMT) on 22nd day of April over selected domain with resolution of 3x3 km of 30 cells. The results showed that there is significance urban heat island presence in Karachi city where the urban area has 5.6°C to 13.5°C higher temperature than its surrounding non-urban areas depending upon the time of a day.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".