Spatial and temporal characteristics of aridity index and association with AO and ENSO in Qinghai Province
Bibliographic record
Abstract
This study were used the methods of climate tendency rate,spatial analysis,Penman-Monteith model,the temporal-spatial variations,cross wavelet and wavelet coherence to analyze the spatial and temporal variations of AI( aridity index),influencing factors,and the relationship of the AI with AO( Arctic Oscillation) and ENSO( El NinoSouthern Oscillation),based on the data of 29 meteorological stations in Qinghai Province during the period of 1961-2013. The results showed that the average of AI was 0. 49 for many years. The trend of AI fluctuantly reduced in the past 53 years. The average of AI linearly decreased at the rate of-0. 03 ·10 a~(-1)( α = 0. 01) over the study area,which means the climate gradually changes to moist in Qinghai Province. The maximum value and minimum value of AI appeared in December and August,respectively,which showed that the average of AI increased firstly then reduced within a year. The maximum of the AI appeared in northwest of Mangai County and north-central of Golmud City and Nuomuhong Town,and the minimum of AI appeared in southern-central of Maqulai County and Jiuzhi County. The average of AI had significantly positive correlation to sunshine time( P 0. 01) and wind velocity( P 0. 05) in Qinghai Province. At the same time,the average of AI has negative correlation with the temperature,precipitation and relative humidity. In addition,the average of AI is multi-scale significant correlation to AO and ENS in this study( a = 0. 05).
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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 | 0.000 |
| 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".