Dynamic Change of Land Use & Landscape Pattern in Middle and Lower Reaches of Shule River During Recent 35 Years
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.000 | 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 it