Bibliographic record
Abstract
The study on the spatial form and agglomeration help to understand the formation of spatial structure of urban.Taking Xi'an City as a case,based on the ArcGIS software,with the aid of Crimestat software,this paper focuses on the spatial distribution,agglomeration characteristics,cluster districts and its formation mechanism and so on.The results show that,distribution of the banks shows the characteristic of layer distribution,and in each circle,most of the banks are distributed around the city trunk roads.Space of banks present an inverted-U-shaped pattern with the mode of extending to the surrounding areas,and agglomeration trend of banks is the strongest when the distance is 4.1km.At the level of small and medium scale in city,it formed multi-level bank clusters in Xi'an City,among them,number of the first order hot spots is larger than the second order,so does the differences of agglomeration degree,and further analysis found that second order hot spots come along with transportation lines and the direction of long axis parallel.Different spatial scales have different formation mechanism of the bank hot spots.As for formation mechanism,the first order hot spots are mainly affected by the traffic convenience,however,the second order hot spots are more concerned about objects that they serving.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".