Development of the Road Network in the City of Salt in 2004 and 2016 Using GIS
Bibliographic record
Abstract
The aim of this study is to analyze the structure of the road network in As-Salt City in the period between 2004 and 2016, in order to identify the road employability in terms of the degree of connectivity, rotation, accessibility, and density. The relationship between the social properties and road distribution are also examined through analysis of the network characteristics concerning population distribution. The data used in this study was based on the As-Salt City Municipality Database supported with fieldwork done in 2016. The network analysis approach using GIS was used to calculate the roads employability. The study compares between the results of the analysis using the cognitive model of the road network for the years 2004 and 2016, knowing that the number of nodes in 2004 and 2016 was constant indicating the number of neighborhoods is 20, while the number of links changed from 42 links in 2004 to 50 links in 2016 and the average center of roads was determined, and it was estimated that the average road center is located near the municipality of As-Salt The study indicates that the road network suffers from a low degree of communication and rotation and the standard distance of road sites in the study area. The standard distance for each group was 2338.49 m. There is a disparity in the distribution of road network within As-Salt City, and the proportion of roads lengths dose not suit the population distribution pattern. The neighborhood of Al- Salalem, includes 19.5% of the total number of roads in As-Salt, because the neighborhood of Al-Salalem contains the highest population census and this is accompanied by urban growth, which is necessarily accompanied by the presence of roads. Therefore, it is recommended to have a plan to redistribute the population in the city and to establish new roads to reduce the problems of traffic in the city.
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How this classification was reachedexpand
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 | 0.002 | 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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".