Stepwise‐clustered heatwave downscaling and projection for Guangdong Province
Bibliographic record
Abstract
Abstract Heatwave events over Guangdong have attracted recent attention under changing climate conditions. It is desired to explore the future changes of heatwave due to its associated consequences on society economy and environment. In this study, a stepwise‐clustered heatwave downscaling approach (i.e., SCHW) was developed for the projection of HWs in Guangdong. The heatwave indicators (occurrence, duration, magnitude, intensity, frequency, and accumulated intensity) were investigated from both the absolute and relative definitions of heatwave events (i.e., AHWs and RHWs) based on the multi‐model ensemble of 13 coupled model intercomparison projected phase 5 (CMIP5) global climate models (GCMs) under two representative concentration pathways (RCPs). The trends (i.e., 0.18–0.29°C per decade under RCP4.5 and 0.30–0.58°C per decade under RCP8.5) of the projected daily maximum temperature indicate that Guangdong would experience continuous warming in the future. The occurrence, frequency, and accumulated intensity of heatwave would increase by 198%, 272%, and 346% in the 2080s (i.e., 2066–2095) under RCP8.5 compared to the historical period (i.e., 1976–2005), respectively. Moreover, the three indicators of HWs are projected to have more substantial increases over inland Guangdong than its costal parts. The spatial variation of occurrence of absolute and relative heatwave under RCP8.5 (2.80 and 2.78) is larger than these under RCP4.5 (2.50 and 2.32) during 2080s (i.e., 2066–2095). The projections of future HWs can help provide valuable information for assessing extreme climate change and identifying desired adaptation strategies.
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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 | 0.001 |
| 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".