An aggregation framework to diagnose the compound hydroclimatic change of the Tibetan Plateau using multiple reanalysis data
Bibliographic record
Abstract
Study region: This study was carried on Tibetan Plateau (TP), which is recognized as the “Third Pole” and “Asia Water Tower”. Study focus: TP is suffering extreme hydroclimatic change under global climate warming. Quantifying the hydroclimatic change pattern and strength is necessary to protect local to downstream hydrology and water resources. This study promoted an aggregation framework to diagnose the compound hydroclimatic change on TP using several high-resolution reanalysis data. New hydrological insights for the region: The promoted aggregation framework is efficient to diagnose the compound hydroclimatic change on TP. Both individual applied data and compound result indicate a warming and wetting environment on most TP, combined with increased trend of evapotranspiration (ET) and soil moisture (SM), while decreased trend of runoff (ROF). The hotspots with strong hydroclimatic change mainly located at inner-southeast TP and northeast TP, including Changtang Plateau, sources of Yellow-Yangtze-Mekong-Salween-Brahmaputra river basin, and Qaidam Basin. The compound warming strength is above 0.06 Celsius/mon/a in winter while the wetting trend is above 0.5 mm/mon/a in summer at most hotspots. ET is increased with trend above 0.1 mm/mon/a in spring, summer, autumn at most hotspots. SM is drying at south periphery while wetting at the remaining area with annual and seasonal trend above 0.0005 mm3/mm3/mon/a at hotspots. ROF is decreased with trend above 2 mm/mon/a at south-southeast hotspots in summer and autumn.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".