Uncertainty Analysis of Humidity and Precipitation Changes using Data from Global Climatic Models with a Case Study
Bibliographic record
Abstract
The process of assessing vulnerability in agriculture, water resources, marine and terrestrial ecosystems, and coastal zone management due to climate change requires construction of predicted vision of climate scenarios under physically reasonable assumptions of greenhouse gas levels. In arid and hot climatic conditions, even minor climatic changes will have significant impact on survival of plant species, wild animals, and other desert ecosystems as well as on human health. The paper presents future changes in temperature, precipitation and humidity in Yemen, Oman, UAE and Qatar under different scenarios using IPCC database derived through GCM's. Various climatic change scenarios developed by IPCC were reviewed, A2 and B2 climatic scenarios were selected for the study. Long-term simulated records derived by the following models were retrieved from the database: 1. Hadley Model -(HADCM3) 2. Canadian Climatic Model - (CGCM2) 3. National Center for Atmospheric Research Model - (NCAR-PCM) Using 1970-2000 values as baseline, variations in 2020-2050 and 2070-2099 were estimated and statistically analyzed to determine uncertainties in prediction. Summarized impacts of climate change on human health based on empirical approach for the region is presented in the paper to make it better prepared (adapted) to the climatic changes with recommendations on future capacity building in modeling and data collection.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".