PERENCANAAN LANSKAP KAWASAN PERKOTAAN KOTA PALU BERBASIS MITIGASI TEMPERATUR PERMUKAAN LAHAN
Bibliographic record
Abstract
Urban landscapes have the potential to provide a variety of benefits for urban communities. Urban landscapes can be a public space that can accommodate various kinds of public activities but also can be an ecological space that can provide space or means of protection for the sustainability of natural and environmental resources. The urban area of Palu city in its landscape arrangement tends to not pay attention to climate aspects as one of the factors that influence environmental sustainability and in creating productive public spaces. The main objective of this study is to develop a landscape plan for the urban area of Palu city based on mitigating land surface temperature. The specific objectives of the study include analyzing and determining the surface temperature distribution of urban areas in Palu City, analyzing the vegetation density index of the urban area of Palu City, formulating concepts and developing urban landscape landscape plans based on land surface temperature distribution and index of vegetation density. The approach used in this study uses a landscape planning approach which consists of several stages starting from inventory or data collection, data analysis, synthesis and planning. Based on spatial data on land surface temperature distribution and vegetation density index results obtained showed the highest distribution of temperature distribution in almost all areas of the East Palu Sub District and parts of West Palu Sub District and the lowest temperature distribution was in parts of West Palu Sub District. Based on the results of the spatial analysis of land surface temperature distribution and vegetation density index, the results show that landscape development plans with intensive green arrangement intensity are located in the East Palu Sub District and part of West Palu Sub District and landscape development plans with the intensity of non-intensive green is located on Most of the West Palu Sub District Area.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".