Signals of Climate Change and Spatio-temporal Analysis of Climate Extremes in the Homogeneous Climatic Zones of Pakistan During 1962-2019
Bibliographic record
Abstract
Abstract This study investigates contemporary climate change and spatio-temporal analysis of climate extremes in Pakistan (divided into five homogenous climate zones) using observed data, categorized between 1962–1990 and 1991–2019. The results show that on the average, the changes in temperature and precipitation are significant at 5 % significance level throughout Pakistan in most of the seasons. The spatio-temporal trend analysis of consecutive dry days (CDD) shows an increasing trend during 1991–2019 except in zone 4 indicating throughout decreasing trend. PRCPTOT (annual total wet-day precipitation), R10 (number of heavy precipitation days), R20 (number of very heavy precipitation days) and R25mm (extremely heavy precipitation days) are significantly decreasing (increasing) during 1962–1990 (1991–2019) in North Pakistan. Summer days (SU25) increased across the country, except in zone 4 with a decrease. TX10p (Cool days) decreased across the country except an increase in zone 1 and zone 2 during 1962–1990. TX90p (Warm days) has an increasing trend during 1991–2019 except zone 5 and decreasing trend during 1962–1990 except zone 2 and 5. The Mann-Kendal test indicates increasing precipitation (DJF) and decreasing maximum and minimum temperature (JJA) in the Karakoram region during 1962–1990. The decadal analysis suggests decreasing precipitation during 1991–2019 and increasing temperature (maximum and minimum) during 2010–2019 which is in line with the recently confirmed slight mass loss of glaciers against Karakoram Anomaly.
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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 | 0.000 | 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".