Long Term Wind Trends Analysis of Coastal Belt of Pakistan
Bibliographic record
Abstract
Pakistan has a long coastal belt, stretched over an area of more than 1000 km from Indian border in east to Iranian border in west, which has varying nature of geomorphic, geologic and climatic setups. In view to understand the characteristic nature of the area in relevance to undertake the wind energy assessment study, it is imperative to carry out the time-series climatic analyses especially for the wind parameters. Pakistan coastal belt has its unique windy nature because of the monsoon period in summer and land-to-sea wind behavior in winter, which further varies respect to coastal geomorphologic features. A research study has been conducted to analyze the long term wind speed trends for the salient cities lying near the coast of Pakistan. The seasonal decomposition technique, i.e. multiplicative model, was applied for the wind trend analyses using the wind data of 60 years for five major cities namely Karachi, Badin & Hyderabad in Sindh province and Lasbella & Ormara in Balochistan province. The present study describes the methodology adopted for the calculation of long term wind speed trends and subsequent the results indicate different wind variables of long term time-series analyses for the selected five cities.
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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) | 0.000 | 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".