Climatic trends in fog occurrence over the Indo‐Gangetic plains
Bibliographic record
Abstract
Abstract The Indo‐Gangetic plains (IGP) in India witness widespread fog during winter months of December–January–February (DJF) since 1970s at temperatures between 5 and 20°C. Despite its vast spatial extent, the localized physical nature of fog over various time and space scales limits successful attempts of its accurate prediction. This poses a challenge towards reducing calamities and huge economic losses associated with the consequent visibility degradation. Increasing rate of urbanization and both enhanced natural and anthropogenic forcing influence fog formation, persistence, and dissipation. It is imperative to understand the associated changes in fog trends to ascertain the effect of these forcing on fog and quantify its prediction. Therefore, the trends in fog occurrence over the Indo‐Gangetic plains are assessed for a period of 37 years from 1977/1978–2013/2014 (DJF). A statistically significant increasing trend in fog frequency is found to be related to changes in associated meteorological parameters. The shift of visibility around year of 1998 is indicated by important changes occurred in temperature, humidity, and wind speed. Distinct patterns before and after 1998 are observed for fog visibility conditions and that may have significant implications for weather forecasts and local climate change.
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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 | 0.000 |
| 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 | 0.000 | 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".