Estimates of Snowfall Depth, Maximum Snow Depth, and Snow Pack Environments under Global Warming in Japan from Five Sets of Predicted Data
Bibliographic record
Abstract
Snowfall depth, maximum snow depth, and snow pack environments under global warming are estimated over all of Japan by using 5 sets of predicted data. The input data used for the estimation were interpolated monthly mean air temperatures and amounts of monthly precipitation under a gradually increasing concentration of CO2 for 100 years from present conditions as predicted by 5 different institutes.The predicted trends varied according to geographic location. In Hokkaido and in the highlands of Honshu, no significant change was found, but the maximum snow depth decreased. In the Tohoku district (northeastern Honshu), except for in the highlands, snowfall and maximum snow depth decreased considrably. Snow pack environments changed from “dry” to “wet”. At low elevations on the side of the Sea of Japan of Honshu south of the Hokuriku district, no snowfall occurred and no snow pack of consequence was present by the mid-21st century. Although details among the 5 sets of predicted global warming data are different for air temperature and precipitation, the results predicted for snow conditions are very similar. For the influences of precipitation, the decrease observed in Canadian Centre for Climate Modelling and Analysis, and the large fluctuations of Australia’s Commonwealth Scientific and Industrial Research Organization, are limited to the winter snowfall depth in Hokkaido and in the highlands of Honshu. In contrast, the influence of the late temperature rising of Meteorological Research Institute (Japan) affects all aspects of snow. Airtemperature is a more important predictor of snow than is precipitation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".