Selection of Typical Meteorological Cycles Based on Comparison of Cumulative Distribution Functions of Different Meteorological Indicators and Assignment of Normalized Modeling Research
Bibliographic record
Abstract
Liaoning region is selected as the study area, and its meteorological data from 1974 to 2024 are used as the study samples.Based on the four indicators, SPI, SPEI, EDDI and CJDI, the normalized composite drought characteristic indicators were constructed by using the CVine joint function and entropy weighted TOPSIS, so as to explore the drought calendar and drought intensity in the Liaoning region, and to analyze the spatial and temporal evolution of the drought cycle.The results showed that the Kendall and Spearman rank correlation coefficients reached above 0.60 and 0.73, respectively, and therefore, the drought duration and drought intensity were strongly correlated.The normalized composite drought characteristics index had a significant negative correlation association with SPI (P<0.01).The normalized composite drought characteristic index has a significant positive correlation association with SPIE (P<0.05).SPI and SPEI are one of the important reasons to study the spatial distribution and temporal pattern of regional drought.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".