STATISTICAL DOWNSCALED LOCAL CLIMATE MODEL FOR FUTURE RAINFALL CHANGES ANALYSIS: A CASE STUDY OF HYOGO PREFECTURE, JAPAN
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
<p>For decades, climate models have been used to understand the present and historical climates, especially global climate models (GCMs). They are used to understand the interaction between climate system processes and forecast future climates. However, the issue of low resolution and accuracy often lead to inadequacy in capturing the variations in climate variables related to impact assessment. In order to capture the local climate changes in Hyogo Prefecture of Western Japan, a local climate modelling based on Second Generation Canadian Earth System Model (CanESM2) was applied using the statistical downscaling technique. Representative Concentration Pathway (RCP) 4.5 and 8.5 scenario were used in generating future climate models. The reliability of models was tested with Linear Regression, Pearson correlation, and Cronbach Alpha. Moderate relationship between rainfall data and both RCP scenarios was found in all chosen stations. Spatial analysis outcome showed that there is a possibility of increase in annual rainfall in Hyogo prefecture, where the increase is significant in Northern region. There is a possibility of increase in maximum and minimum temperature in four selected stations due to the increase of greenhouse gas emissions.</p>
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 it