LONG TERM TRENDS OF ANNUAL AND MONTHLY PRECIPITATION IN JAPAN<sup>1</sup>
Bibliographic record
Abstract
ABSTRACT: Long term trends in Japan's annual and monthly precipitation are investigated in this study. The statistical significance of a trend at a study site is assessed by the Mann‐Kendall (MK) test, and field significance of trends in climatic Regions II, III, and IV is evaluated using the bootstrap test preserving cross correlation. The practical significance of a trend is judged by a percentage change of the sample mean over an observation period. The field significance assessment demonstrates that annual precipitation in Region II did not show any significant change, but regional precipitation shifts occurred in different months. Precipitation significantly increased by 12.2 percent in May, while it significantly decreased by 12.0, 10.5, 15.6, and 19.7 percent, respectively, in April, September, October, and December. In Region III, annual precipitation declined by 11.8 percent, and monthly precipitation significantly decreased from September through January and in April, with the greatest decrease (38.2 percent) in December. In Region IV, significant reductions occurred in both annual precipitation (by 15.6 percent) and monthly precipitation from September through February and in June and July, with the worst reduction (44.7 percent) in December.
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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 | 0.001 | 0.001 |
| 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".