A stepwise-clustered heat stress downscaling approach to analyze future variations of heat stress in East China
Bibliographic record
Abstract
Abstract In this study, the fifth generation ECMWF reanalysis (ERA5) reanalysis datasets and three global climate models (GCMs) were selected as the inputs of the stepwise-clustered heat stress downscaling (SCHSD) method to simulate the future heat stress indices in East China. The heat stress indices included the Heat Index (HI), Humidex (HUMIDEX) and the simplified Wet Bulb Globe Temperature (sWBGT). Three GCMs (i.e., CanESM5, INM-CM4-8 and MPI-ESM1-2-HR) under two Shared Socioeconomic Pathway (SSP245 and SSP585) were input into the SCHSD model to develop downscaled climate projections. To verify the SCHSD model, the reproduction results from three GCMs during the period of 1990–2014 were compared to daily observational data (i.e., HI, HUMIDEX and sWBGT). The verification results suggest that the coefficient of determination (R2) of the stations in the northern part of East China mostly exceeds 0.8, while the R2 of the stations in the southern part of East China is mostly in the range of 0.6 to 0.8. The projection results suggest that the future heat stress in East China would generally maintain an upward trend from 2021 to 2100. The largest change in heat stress is projected in June under the period of 2021–2100. The results also show that the highest average of days in the danger category is 78.9 in summer under the SSP585 scenario, accounting for 86% of the total summer (June, July and August).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".