Prediction of climate change scenarios in Varanasi District, U. P., India, using simulation models
Bibliographic record
Abstract
Climate change refers to climatic fluctuations over a long period of time such that shift in the type of climate may occur over an area.Its effect may be decisive both globally and regionally.Climate change scenarios are a powerful tool for understanding climate change, charting response strategies and supporting climate policy making.Thus, the present research was conducted to foretaste climate change scenarios in Varanasi district, during 2015-2054 using simulated data of 5 CMIP3 GCMs and weighted ensemble method in comparison with observed data.Weighted ensemble method is also method, for diminishing uncertainty in simulated results.Their simulated monthly climatic parameters, that have been received from the CCCSN website of Canada, were downscaled by bilinear interpolation.Then results of 4 downscaled climatic parameters in seasonal and annual scale were validated along with their observed data using statistical formula (R, D, MSD, SB, SDSD and LCS).Results showed that, weighted ensemble method, is generally desirable and proper method, in reducing uncertainty in simulated results.According to the results obtained for the period 2015 to 2054, climate of Varanasi district will experience increased temperature in four seasons and a decrease in rainfall for SW monsoon and post-monsoon seasons.In addition, a decrease is anticipated in relative humidity in winter and summer seasons.Furthermore, an increase was observed in predicted sea level pressure for post-monsoon season, summer and SW monsoon seasons.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".