Comparative Analysis on Four Distribution Simulating Quarter Rainfall——Example for Yibin City
Bibliographic record
Abstract
The probability of precipitation is an important part of weather forecast and drought and flood disasters risk assessment.Through the researches Comparative Analysis on Four Distribution Simulating Quarter Rainfall in Yibin,the authors expected to provide some scientific bases for the local disaster reduction and agricultural production.with 51 years' quarter precipitation data of Yibin city in Sichuan province for 1960-2010,normal distribution,Gamma distribution,logarithmic normal distribution,Pearson-Ⅲ distribution are applied to study the region's precipitation distribution characteristics.On this basis,the ordinary least square method is used for goodness of fit test of the four distribution functions.The results show that the four distribution functions all can be fit with quarter precipitation distribution of Yibin city.Through goodness of fit test shows that,the optimum fitting distribution function,in turn,are gamma distribution,normal distribution,logarithmic normal distribution and Pearson-Ⅲ distribution in each season.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".