Spatio-temporal Analysis of Hazardous Fog: A Case Study of Bahawalpur, Pakistan
Bibliographic record
Abstract
This study focuses on visibility and duration of fog prevalence, using secondary data for Pakistan in general and primary data for Bahawalpur district, Punjab province. The hazardous nature of fog was probed by calculating the hazardous fog index (HFI); index values were computed using 17 years' worth of temporal data from 1997/98 to 2013/14, recorded by the authors at a manual observation station in Bahawalpur district. The results show significant variations in fog hazard across years. Fog that persisted for more hours and created lower visibility was associated with high HFI values, indicating its more hazardous nature in several respects from the point of view of human activities. The study highlights some significant impacts of fog on various aspects of human life and suggests some protective measures and fog-dispersal techniques.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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 teacher head, 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".