Evaluating climatic warming and the modulating effects of surface water and regional variables in western Bangladesh
Bibliographic record
Abstract
Rising temperatures in western Bangladesh (2001–2023) were analyzed to explore interactions among climatic factors, external influences, and surface water bodies. This study enhances understanding of regional climate dynamics amid urbanization, changing topography, and shifting land use, which challenge climate resilience. Remote sensing and meteorological data across 64 districts were integrated, employing various analytical approaches, including non-parametric trend analyses such as the Mann-Kendall test and Sen's slope estimator, to assess changes in land surface temperature (LST) and precipitation. This comprehensive methodology facilitated the capture of spatial and temporal variations across seasonal periods, with particular emphasis on the warmer months. Significant warming trends were observed, particularly during the pre-monsoon and monsoon seasons, with a strong inverse relationship between surface water area and LST-Day. A clear longitudinal pattern emerged, showing an inverse correlation (-0.80) between maximum air temperature and longitude in March, contrasted by a positive correlation (0.71) for relative humidity during the same period. These trends intensified in May, with correlations reaching -0.96 for temperature and 0.94 for humidity. These spatial patterns underscore the vital role of surface water and topography in regulating temperature extremes, emphasizing the need for localized climate adaptation strategies. District-level analyses, such as those in Faridpur and Chuadanga, revealed notable year-to-year variations in temperature and precipitation. The findings indicate that specific local factors, such as surface water bodies and regional influences like longitudinal gradients, significantly shape microclimates in western Bangladesh. These insights offer valuable implications for urban planning and climate resilience strategies.
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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 | 0.002 |
| Science and technology studies | 0.000 | 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".