VISUAL CHARACTERISTICS OF PHYSICAL CHARACTERISTICS OF GAJAH MADA ROAD CORRIDOR IN DENPASAR CITY
Bibliographic record
Abstract
<p>Economic development in urban areas indirectly changes the function of residential areas into commercial areas. Along with the development of the function of the Gajah Mada area, the threat of decreasing the visual quality of Jalan Gajah Mada as an artifact of the corridors of the Denpasar Old Town area naturally cannot be avoided. The need for increased trading space, high building density, development of economic functions that are more commercial in nature due to the demands of profit and modernization as well as a slum environment can also eliminate the visual character of the physical corridor of Jalan Gajah Mada which still retains the characteristics of Balinese architecture and the image of Kota Tua because Therefore, it is necessary to study the visual characteristics of the physical corridors of Jalan Gajah Mada so that directions regarding the arrangement should be considered in accommodating changes in the physical corridors so that they remain visually aligned. This research method is a type of descriptive research using qualitative methods. Descriptive research is a research that aims to provide a systematic, factual, accurate description of the facts and characteristics of the population so that it can produce the physical characteristics of the Jalan Gajah Mada corridor as seen from the path elements, architectural patterns and street trees that surround the corridor.</p>
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".