The Precipitation Variations in the Qinghai-Xizang (Tibetan) Plateau during 1961-2015
Bibliographic record
Abstract
The variation of precipitation plays an important role in the eco-hydrological processes and water resources regimes on the Tibetan Plateau (TP). Based on the monthly mean precipitation data of 65 meteorological stations over the TP and surrounding areas from 1961 to 2015, variations, trends and temporal–spatial distribution of precipitation have been studied; furthermore, the possible reasons were also discussed preliminarily. The results show that the annual mean precipitation on the TP was 465.5 mm during 1961–2015. The precipitation in summer (June–August (JJA)) accounted for 60.1% of the whole year’s precipitation, the precipitation in summer half-year (May–October) accounted for 91.0%, while the precipitation in winter half-year (November–April) only accounted for 9.0% of the whole year’s precipitation. During 1961–2015, the annual precipitation trend was 3.8 mm/10a and the seasonal precipitation trends were 3.0 mm/10a, 0.0 mm/10a, −0.1 mm/10a and 0.4 mm/10a in spring, summer, autumn and winter on the TP, respectively. The precipitation has decreased from the southeastern to northwestern TP; the trend of precipitation has decreased with the increase of altitude, but the correlation was not significant. The rising of air temperature and land cover changes may cause the precipitation by changing the hydrological cycle and energy budget. Furthermore, different patterns of atmospheric circulation can also influence precipitation variation in different regions.
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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 | 0.002 |
| 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".