Response of net primary productivity to climatic factors in Qilian Mountains in resent eleven years
Bibliographic record
Abstract
Based on the model of Carnegie Ames Stanford Approach(CASA),the net primary productivity(NPP)of Qilian Mountains was estimated with the data of MODIS(from NASA)and meteorological data(from China meteorological science data sharing service)from 2003 to 2013. The response of NPP to climatic factors was further analyzed with the simple correlation analysis and partial correlation analysis. Results show as follows:(1)there exist obvious annual change of Net primary productivity in Qilian Mountains,with which the growing season is concentrated on five months of a year from May to September. The average values of net primary productivity in July is above 89.96 g C·m-2in the east and middle of the Qilian Mountains,while only 12.10 g C·m-2in Da Chaidan Station in the west of Qilian Mountains.(2)the simple correlation analysis showed that the sensitivity of net primary productivity to the change of temperature was more obvious than that to the change of precipitation in Yeniugou,Wushaoling and Qilian,which was opposite to the phenomenon in Gangcha,Tuole,Dachaidan and Menyuan that the sensitivities of net primary productivity to the change of precipitation was more obvious than that to the change of temperature.(3)there are obvious lag effects of the responses of net primary productivity to temperature and precipitation in Qilian Mountains,and the lag stages were one month or three months,and the lag effects of net primary productivity to cumulative precipitation were differ from one month to five months.(4)the results of partial correlation analysis showed that the net primary productivity to temperature when controlling the precipitation influence in growing season was obviously correlated in Qilian,Yeniugou,Wushaoling and Gangcha,while the net primary productivity to precipitation when controlling the temperature influence was obviously correlated in Gangcha,Yeniugou and Tuole,of which the correlation degrees were the same with those of the simple correlation analysis.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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".