Searching For The Nearest Route To The Location Of Health Facilities Using The Djikstra Method
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A health facility or health service facility is a tool or place used to carry out health service efforts, both in terms of promotive, preventive, curative and rehabilitative carried out by the central government, regional government or the community. This research contains development applications that cover every hospital, health center, and practice clinic located in the Langkat Regency area with the aim of facilitating the community in finding the nearest hospital, health center, and practice clinic. The application built can display the location of the health facility in map form and can display information in the form of name, address, telephone number, photo of the health facility, services available there, working hours, and further information on the place. In this study, the search for health facilities is only subject to distance as a health facility criterion, so that it can be developed further. To find the closest route to a health facility, here the author uses the Dijkstra Algorithm method which has been widely researched to be applied to the shortest route search system. This algorithm was invented by Edsger Dijkstra, a computer scientist from the Netherlands. The way Dijkstra's algorithm works is with a greedy strategy, namely at each step it chooses the side with the smallest value that connects the selected and unselected nodes/nodes. This algorithm requires a point of origin and a destination with the final result being the shortest distance from the point of origin to the destination along with the route.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it