The Application Research of the TRMM Precipitation Data in the Provincial Scale—A Case Study of Liaoning Province
Bibliographic record
Abstract
According to the monthly precipitation data of Tropical Rainfall Measurement Mission(TRMM) 3B43 from January 1998 to December2010,a case study as the provincial scale,the change of recent 13-year precipitation in Liaoning Province were analyzed by means of the liner climatic tendencies ratio method and Geographical Information System(GIS) spatial analytic techniques. Some conclusions can be drawn: The linear correlation between TRMM precipitation data and observed data and is very high,the data has a higher accuracy; The annual precipitation in Liaoning Province is mainly between 425-1 082 mm,and the overall performance is the trend of decreasing from the southeast to the northwest; The main precipitation concentrate in May to September of each year,the precipitation during this period accounted for more than 70% of the total annual precipitation,among them,the maximum precipitation concentrate in July,to 20% of the annual precipitation,and in February the minimum,less 2% of the annual precipitation,seasonal change is obvious; In recent 13 years,the average annual precipitation showed increasing trend in Liaoning Province. However,the summer precipitation showed a decreasing trend,the precipitation in the western and middle Liaoning Province is particularly evident. These indicate that in recent 13 years,the phenomenon of regional drought in spring and summer is serious.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".