Surface urban heat island intensity in five major cities of Bangladesh: Patterns, drivers and trends
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
There is currently a lack of knowledge regarding the spatiotemporal variation of day and night surface urban heat island intensity (SUHII) in the major cities of Bangladesh. These cities have a large population base and generally lack the resources to deal with rapid urbanisation impacts, so any increase in urban temperature has the potential to affect people both directly (due to heatwave conditions) or indirectly (due to loss of livelihood). Time series diurnal (day/night) MODIS land surface temperature (LST) data for the period 2000–2019 was used to produce baseline information about SUHI intensity, drivers and temporal trends. Five large cities were selected based on population size and historical urban expansion rates. Results indicated that annual SUHII was greater in the larger cities of Dhaka and Chittagong than in the smaller cities. SUHII observed during the day was also greater than at night. Population (in terms of city size and surface cover), lack of greenness and anthropogenic forcing were major factors affecting SUHII. Trend assessments revealed positive trends during daytime in four out of five cities, while one city recorded negative trends at night. The findings may provide new insights into impacts arising from rapid urbanisation and demographic shifts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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