Peran Lansekap Dalam Kinerja Infrastruktur Perkotaan Studi Kasus: Surabaya dan Malang, Indonesia
Bibliographic record
Abstract
Transportation has been recognized as one of the indicators instrumental in the development of the city. However, the development of transport seems to have the impact on the environment in the spatial and temporal large of coverage (Rini , 2005) . The impact of high transport movements will contribute to vehicle air pollution, thermal energy (temperature) and noise (Soedomo, 1999). In Indonesia, stations, airports, public transport infrastructure and other terminals, has a noise level of up to 70 dB (SK.MLH 24/11, 1996). Landscape Infrastructure is one of the strategies new urban designs to extend the performance parameters of a landscape that is designed for high-performance multi - system function, including those originally thought to be derived from the traditional system infrastructure. Thinking in terms of Landscape Infrastructure adds several advantages to traditional infrastructure : the beauty of the city and re-vegetation/forestation, water and energy conservation; restoration of natural systems, storm water management, agriculture, energy, expansion of wildlife habitat; favoured the use of pedestrians , and expanded parks and open space areas built ignored by the existing urban infrastructure (Aquino, 2011) . This paper will discuss and how to optimize the design of the infrastructure landscape for urban transport infrastructure to minimize air pollution, noise and energy conservation, in terms of transport infrastructure in Malang and Surabaya, Indonesia
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".