Spatiotemporal Change of Water Budget in Gansu Province in Recent 51 Years
Bibliographic record
Abstract
Based on the daily data from 27 meteorological stations in Gansu Province during the period from 1960 to 2010,the values of evapotranspiration were calculated by applying Penman-Monteith model,and then the climate water budget was obtained by subtracting evapotranspiration from precipitation during the same period. Spatiotemporal water budget over Gansu Province was analyzed using the Mann-Kendall abrupt test,wavelet analysis and GIS spatial interpolation means. The correlation coefficients and linear regression analysis were used to discuss the dominant factors affecting water budget. The results showed that,during the period from 1960 to 2010,the multiyear average water budget over Gansu Province varied in a range from- 194 mm to- 1 293 mm,it was decreased by 6. 48 mm every decade,and the water loss was spatially increased from the southeast to the northwest. Water loss was gradually increased from the 1960s,decreased in the 1970s,increased continuously after the 1980s,and reached the maximum value in the first 10 years of 21 century. Average seasonal water loss was in an order of summer spring autumn winter. M-K abrupt test indicated that a sharp change and sensitive period of water budget occurred around 2008. Morlet wavelet analysis revealed that there were the obvious 4. 87-year and 4. 52-year periods of average water deficit over Gansu Province,which was mainly affected by the atmospheric circulation. Precipitation,sunshine duration and average humidity were the dominant factors affecting water loss.
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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 | 0.001 |
| 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".