Spatial-temporal change and its driving forces in woodlands in the Shangluo Section of the Qinling Mountains over the last 30 years
Bibliographic record
Abstract
Based on four remote sensing images (MSS in 1978 and TM in 1990,2000 and 2006),the spatial-temporal changes of woodlands in the Shangluo section of the Qinling Mountains were analyzed with the support of ERDAS and GIS.Over the past 30 years,the total area of the woodlands increased from 12584.55 km2 to 14479.44 km2.The area of coniferous forests and shrubs both increased,while the area of coniferous-broad mixed forests and latifoliate forests decreased.The change of various woodlands in the low-elevation regions was more serious than that in high-elevation regions.The woodlands became fragmented and their internal structure has fundamentally altered,i.e.most of the latifoliate forests have been replaced by coniferous forests.The major driving forces for such changes are from regional socio-economic resources,farmers' incomes and the demands for arable land.
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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 | 0.000 |
| 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".