Precipitation trends and teleconnections identified using quantile regressions over Xinjiang, China
Bibliographic record
Abstract
ABSTRACT Precipitation in Xinjiang, China, was modelled with covariates, such as time and climate indices, using quantile regressions. Compared to a frequentist quantile regression, a Bayesian quantile regression tended to generate smoother and narrower band confidence intervals of quantile regression coefficients, especially at extremely high and low quantile levels. A full picture of temporal trends at quantile levels from 0.01 to 0.99 indicates that the wet season (May to August) precipitation in Northern Xinjiang and the western regions of Southern Xinjiang showed statistically significant increases with different magnitudes over all quantile levels. However, the wet season precipitation in South‐eastern Xinjiang decreased at some quantile levels. The Eastern Atlantic/Western Russia (EAWR) pattern was the most significant large‐scale climate pattern that influenced wet season precipitation when compared to other studied patterns, i.e. the El Niño–Southern Oscillation (ENSO), the Atlantic Multidecadal Oscillation (AMO), the Pacific Decadal Oscillation (PDO), the Northern Oscillation (NO), the Arctic Oscillation (AO) and the North Atlantic Oscillation (NAO). The quantile regression coefficients associated with theEAWRindex positively increased from low to high quantile levels. TheENSOsignificantly affected the extremely high wet season precipitation in Xinjiang. El Niño increased and La Niña decreased wet season precipitation in Northern Xinjiang, with different magnitudes at different quantile levels. Other climate patterns, i.e. theAMO,PDO,NO,NAOandAO, did not evidently affect the wet season precipitation conditional on theENSOandEAWR. These findings suggest that the predictability of seasonal precipitation over Xinjiang can be improved by incorporating indices associated with theENSOandEAWRas model predictors.
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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) | 0.000 | 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".