A Study of Anomalous Wet and Dry Years in the Winter Precipitation of Pakistan and Potential Crop Yields Vulnerability
Bibliographic record
Abstract
Pakistan experiences distinctively large rainfall variability on spatial as well as temporal scales. On spatial scale the rainfall variability is mainly caused by its peculiar topographic features encompassing from south to north of the country. On the other hand the temporal rainfall variability sometimes exceptionally large, does affect the climate of the country that in turn impacts the climate-dependent sectors like agriculture, hydroelectric power generation and ecology. In this study the 30-year winter season (December-March, DJFM) rainfall data of 35 meteorological sites of Pakistan have been analysed to identify the anomalous wet and dry years, their potential impact on crop yields across Pakistan and vulnerability of climate. The National Centre for Environmental Prediction (NCEP), US reanalysis data are used to investigate the association of the surface and upper air atmospheric circulation features responsible for anomalous wetness and dryness. This study may prove of some help for an improved winter rainfall prediction tool and better management of available water resources viz-a-viz optimal crop yields production.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 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".