PERBANDINGAN KARAKTERISTIK POLA PENYEDIA INFRASTRUKTUR PERMUKIMAN PADAT TINGGI DAN RENDAH KASUS DI DANUKUSUMAN DAN MOJOSONGO KOTA SURAKARTA
Bibliographic record
Abstract
Surakarta has five subdistrict. Subdistrict with the highest density is Serengan and subdistrict with the lowest density is Jebres. In Serengan, Danukusuman is the crowded, whereas in Jebres, Mojosongo is the lowest density. Density differences problem can result in differences in the activity of the people who live. Then can affect the differences in infrastructure needs and patterns of settlement there is the provision of infrastructure (actor variations and details of cooperation). So, this study done to know the characteristic comparison of high and low density settlements infrastructure provision pattern in Danukusuman and Mojosongo. To gain it, carried out was used a comparative analysis of settlement characteristics both in terms of physical, economic and socio-cultural. further analysis is to compare the characteristics of the infrastructure available with the applicable standards (SNI). then, final analysis is to determine and compare the pattern of provision of infrastructure and settlements in Danukusuman and Mojosongo. the results of the analysis is known that that there was no significant difference between high dense settlements (Danukusuman) and low dense settlements (Mojosongo) both from the aspect of the character of the location, infrastructure preparation and provision pattern. Only difference is quite prominent at the level of the economy and the provision of communal wastewater infrastructure that influenced by the density of settlement, while the difference in the provision of clean water is more influenced by the character of the location / the physical settlement. Keywords: high and low density, pattern of provision of infrastructure settlement infrastructure,
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.002 |
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".