Multi-level trend analysis of extreme climate indices by a novel hybrid method of fuzzy logic and innovative trend analysis
Bibliographic record
Abstract
Multi-level trend analysis of extreme climate variables is an efficient method for in-depth investigation of the climate change impacts on ecohydrology. However, most of existing statistical methods do not reveal potential trends in different levels of data. In this study, a new approach namely Fuzzy Innovative Trend Analysis (FITA) was introduced that takes the advantages of fuzzy logic to improve and facilitate Innovative Trend Analysis (ITA) abilities to multilevel trend detection at Extreme Climate Indices (ECIs). Regarding the graphical nature of the proposed method, two new indices, namely Grow Percent (GP) and Total Grow Percent (TGP) were suggested for quantifying the power of trend at distinct levels. The FITA was utilized for trend detection at three levels of four important ECIs related to precipitation and temperature. To this end, long-term (1960-2021) daily temperature and precipitation observations at six meteorology stations across diverse climatic zones of Iran were used. The multilevel trends attained by the FITA were further compared to those of ITA, Mann-Kendall (M-K), and Sen's slope (SS) tests. The results indicated that the FITA provides promising results with higher interpretability and reliability than its counterparts at all stations. The underlying high-resolution trends detected at certain stations also pointed out that the M-K and SS tests may yield in misleading interpretations when they are used for identifying trends in ECIs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".