SPATIAL ACCESSIBILITY OF LARGE GENERAL HOSPITALS IN CHANGCHUN CITY CENTER BASED ON STREET NETWORK CENTRALITY
Bibliographic record
Abstract
Street network centrality is calibrated in a multiple centrality assessment model(MCA)composed of multiple measures such as closeness, betweenness, and straightness. By using UNA developed by MIT,the paper will discuss the spatial accessibility of large general hospitals from the perspective of street network centrality. Firstly, the paper examines the street centrality indices in Changchun. Secondly, by creating buffer of every large general hospital, the research will calculating the average value of the street centrality indices(closeness and betweenness), which can be used as the quotas of the spatial accessibility of large general hospitals. Thirdly, the Kriging method is applied to street centralities so that the spatial distribution characteristics of it can be explained. Besides, the relationship between the spatial accessibility of large general hospitals and the distances to Renmin square are showed by correlation coefficient and regression model.Fourthly, all of the street network distance of every large general hospital to Renmin square is divided into eight segments to calculate the average value of the spatial accessibility of large general hospitals, which will be z-standardized. And then the conditions of two spatial accessibility indices of large general hospitals with the change of distances to Renmin square will be compared. Lastly, the spatial accessibility of large general hospitals will be classified into eight types, including H-H-L, H-H-H, H-L-L, H-L-H, L-H-L, L-H-H, L-L-L,L-L-H.
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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 | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".