Future variations of water stress over China will have impacts on the north-to-south shifts in agriculture and manufacturing sectors
Bibliographic record
Abstract
Abstract In this study, a fuzzy technique for order preference by similarity to ideal solution water stress prediction (FTOPWSP) index is developed to analyze spatiotemporal variations of water stress, and concomitant effects on the populace within China under climate change. The potential implications of water stress variations on the migration of the agriculture sector, manufacturing industry, and human population are further discussed. Our results show that the value of the FTOPWSP index could decrease by 7.17% from 2020 to 2099 under the representative concentration pathway 2.6-shared socioeconomic pathway 2 (RCP2.6-SSP2) scenario. Such a decrease signifies an augury of water stress in the ensuing eight decades. The primary causative factors are attributed to decreased water resources supply, such as groundwater recharge, groundwater runoff, and subsurface runoff. Moreover, future annual variations of the FTOPWSP index value are predominantly contributed by its variations during the spring and autumn seasons. The water stress in the southeastern provinces would be much lower than the northwestern ones, wherein more than 20% of the entire Chinese population would be severely impacted by water stress. More importantly, such variations of water stress could lead to the north-to-south migration of the agriculture sector, manufacturing industry, and human population.
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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 | 0.000 |
| 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".