Analysis of Spatial Distribution of Rural Settlements and Its Influential Factors in Qinba Mountain Area——A Case Study of Ningqiang County in Shaanxi Province
Bibliographic record
Abstract
This paper,studying on the spatial distribution of different rural settlements,can provide significant reference for immigrant relocation project,integration of rural settlement distribution and intensive utilization of land.Based on the second land survey data and SRTM-DEM,and by using average nearest neighbor index(ANNI),kernel density estimation(KDE),exploratory data analysis(EDA)and‘hotspot'analysis,the characteristics of the spatial distribution of rural settlements and its influential factors in terms of natural environment and social economic conditions were studied.The results show that the distribution of rural settlements has an‘arched and layered'structure and marks difference of the spatial distribution and scale in Ningqiang County,big plaques are mainly in river terrace and low mountain areas,high density,small plaques and low intensive land use were influenced by the dominant factor—terrain.Rural settlements can be classified into three types by the principles of suiting one′s measures to local conditions:positive development,internal potential tapping and strange land relocation.The study results can provide reference for the construction and development of the small cities and towns.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".