Response of 50-year Regional Climatic Variation to Global Warming in the Desert Area of Northwestern China
Bibliographic record
Abstract
The climate in desert is totally different from those in other landscapes. This paper, by analyzing the data collected in Minqin desert area during the period of 1961-2009, attempts to figure out how the temperature, precipitation and air humidity in the desert area of northwestern China respond to global warming. The result revealed that: the increasing rate of the mean annual temperature in Minqin during 1961-2009 was greater than that of the global average and that of the hundred-year average in China; whereas lower than that in the arid area in northeastern China in the recent 20 years. In Minqin, the temperatures in winter and spring rose significantly. The winter and spring temperatures and the annual maximum temperature showed big variations, with great fluctuations. The annual air humidity increased dramatically while the precipitation displayed an insignificant increasing trend. There was a significant correlation between air humidity rising and precipitation increasing. However, there’s no sound consistency between the periodic variations of air humidity and those of precipitation. The warmer spring usually induced intensive sandstorms, and the instability of air temperatures has caused yield reduction of crops/ fruits in the study area.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.000 | 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".