Analysis of land use difference degree in arid inland river basin——Taking Zhangye,Ganzhou District as an example
Bibliographic record
Abstract
The land in arid inland river basin is exposed to harsh natural environment,where the ecological environment is fragile,human activities caused great harm.Researching arid inland river basins and their regional differences in the degree of land use can give the theoretical basis for reasonable development of land use and land use planning.Taking Ganzhou District as an example,we used GIS technology and comprehensive analysis method,composited index with a model of land use degree,land use change models and the extent of regional differences in land use change model to analyze the extent of land use and regional differences.The results showed that in 2005 the average land use degree index an of the whole region was 255.25,the overall level of land use was at a medium level,but the villages and towns land use levels were not the same,needing further development and utilization of land resources,and land use was in the development period.In Ganzhou District most of the villages and towns land use degree changed and land use degree change rate were greater than zero,land use varied quite differently;Land use degree distributied according to different characteristics of topography,a high degree land use was focused on the central plains region,and north and south sides of mountainous areas were in the lower level of land use.
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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 | 0.002 |
| 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".