Temperature variability over urban, town, and rural areas: The case of Pakistan
Bibliographic record
Abstract
Abstract This study investigates the evolution of temperatures at several locations in Pakistan. Data on annual and seasonal minimum (Tn) and maximum (Tx) temperatures from 1950 to 2013 of urban (16 stations), town (11 stations), and rural (nine stations) areas are analysed to establish the mean decadal rate of change in urban, town, and rural temperatures. The homogeneity of the data was assessed using HOMER 2.6. To measure the temporal intensity of change in temperature, the data were split into two different periods: 1950–1981 (P1, phase of less urbanization) and 1982–2013 (P2, phase of highly urbanized period) and were analysed separately for both phases of 32 years each. The per decade changes of annual minimum and maximum temperatures (dTna and dTxa, respectively) over most stations show an increase in temperatures. The trends of annual and seasonal dTn and dTx observed over urban, town, and rural stations during P2 are significantly higher than those observed during P1. The increase in minimum temperatures is more significant than that in maximum temperature, and it is also more significant on urban stations than the town and rural stations. However, the maximum temperatures increase more at town stations than urban and rural stations. Overall, the tendencies in temperatures reflect less change in summer temperatures than other seasons of the year over the whole period.
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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 | 0.001 | 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".