Bibliographic record
Abstract
Using the Landsat-TM remote sensing data,The land use/land cover change( LUCC) of Ejin natural oasis in 1980s,1900 and 2009 were interpreted. In addition,the vegetation cover( VC) was extracted,and then evapotranspiration( ET) inverted with SEBS. With the contrast of 7 field verification points and 11 Google Earth verification points,the image interpretation and remote sensing inversion ET in 2009 were accurately verified. ET remote sensing inversion accuracy is higher,the accuracy of imagery interpretation is 77. 78% and average relative error is 8. 72%,and it accords with the research of the accuracy requirement. At the same time,the influences of the spatial and temporal patterns of ET,VC,LUCC and LUCC on ET and VC were discussed.The ET and VC space distributions were related to the type of LUCC,high ET value areas were mainly in the 19branch river of the East Juyanhai,( Sogo Nur),East River( Naryn River),Nishikawa( Mulin River),and forest land,VC larger shrub and grass. And low ET value areas were mainly in the desert,residents,VC small shrubs,degradation serious grassland and a small amount of cultivated land. ET and VC distribution were generally consistent except for water area. Therefore,the spatial patterns of LUCC basically controled the distribution characteristics of ET and VC in the study area.
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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) | 0.001 | 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".