Dynamic changes of land use and landscape pattern in Taolai River Basin in the recent 30 years
Bibliographic record
Abstract
Based on the MSS images in 1976 and the TMimages in 1989,2000 and 2010,the changesof land use/coverage and landscape pattern and their driving force in Taolai River Basin were analyzed from 1976 to 2010 by using principal component analysis method in combination with landscape indexes and the methods of variation amplitude,dynamic degree and transfermatrix.The results indicated thatduring the recent34 years after1976,the proportionsof cultivated land and construction land expanded sharply from4%and 0.04%to 7.4%and 0.26%,respectively;while the area of glaciers and permanent snow and grassland decreased by 897.98×104hm2and 383.7×104hm2,respectively.Among the various typesof land use,construction landwas the highestin dynamic degree(16.13%),followed by cultivated land.The conversion of glaciers and permanent snow into bare rocks,the conversion of gobi into cultivated land and the conversion between forestland and grasslandwere the main trendsof land use variation.The patch density of total landscape increased at first and decreased later,while the largest path index decreased at first and expended later.Therefore,the shape of landscape became more and more irregular,and the degree of landscape diversity decreased at first increased later.The population growth and economic development were the most direct driving forces of land use/coverage changes in Taolai River Basin,and climatic factors also affected land use/coverage changes to some extend.
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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".