Dynamic Change of Land Use & Landscape Pattern in Middle and Lower Reaches of Shule River During Recent 35 Years
Bibliographic record
Abstract
The middle and lower reaches of Shule River was chosen as the study area.Remote sensing images in 1975, 2000 and 2010 were used to extractland use/cover information. Then the landscape change tendency, change area, change rate and specific conversion type werestudied quantitatively through the transfermatrix calculation. Through extracting characteristics of landscape patternindexes, the regional pattern of landscape ecology and landscape heterogeneity was analyzed. Finally, the driving factors for landscape pattern change were investigated. Results indicated that the proportions of cultivated land and construction land expanded sharply by 19.6% and 73.3%, respectively over the past 35 years. Construction land was the highest in dynamic degree,reaching 2.11% and was followed by cultivated land. The conversions of gobiand grassland into cultivated land,and the conversion of gobi into grassland and construction land were the main trends of the land use variation. Totally, landscape density increased, the largest path index decreased, the weight area index increased and the shape of landscape became irregularity. The degree of diversity landscape and fragmentation increasing also showed that the land uses became more complex.Driving force analysis showed that the population growth and economic development were the most direct driving forces for land use/cover changes in 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".