Climatic Variability and Linear Trend Models for the Five Major Cities of Pakistan
Bibliographic record
Abstract
Estimates show that the average temperature of Earth’s near surface air and oceans has raised by 0.74 ± 0.180C during last 100 years. In this work the impact of the same has been explored for major urban areas of Pakistan. For this exploration long-term mean, mean-maximum and mean-minimum temperatures for the period 1961 to 2007 have been studied. The precipitation in the major cities of Pakistan, are also studied. The maximum increase in mean temperature is found to be 0.0570C per year (in Quetta). The minimum increase is found to be 0.0190C per year (in Peshawar). Both these increments are more than the global mean. An isolated discrepancy is found in mean maximum temperature of Lahore but the same can be explained in terms of heavy and prolonged monsoon rains. Moreover, precipitation in Karachi is found to be decreasing that needs to be further explored.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".