Extreme index trends of daily gridded rainfall dataset (1960–2017) in Taiwan
Bibliographic record
Abstract
Abstract Previous lectures have shown that to effectively explore Taiwan’s climate change or other relevant topics, long-term and stable observation datasets are required. We introduce the high-resolution grided precipitation dataset (TCCIP_PR), which was constructed by the Taiwan Climate Change projection and adaptation Information Platform (TCCIP) program from thousands of station records. Although, a high spatial-time relationship exists between the TCCIP_PR and the stations, a large uncertainty occurs over the complex terrain on the southwest windward side during the summer, due to sparse stations. To better understand the change in the extreme rainfall trends, we analyze 9 suitable indices from the Expert Team on Climate Change Detection and Indices (ETCCDI). Our result show that the extreme rainfall intensity and frequency have continuously increased for a long time, and the consecutive dry days have decreased in recent decades, particularly over southwest Taiwan. The regime change evaluations agree that the precipitation characteristics were amplified and become more unpredictable from the early (1960–2002) to the late (2003–2017) period. For future applications or research, the calculated results of the extreme indices can be found in the printed documentation and the online retrieval system.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.001 | 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".