EVALUASI KONSEP GREEN OPEN SPACE TERHADAP KUALITAS TAMAN PADA PROGRAM PENGEMBANGAN KOTA HIJAU (P2KH) KECAMATAN KENDAL (STUDI KASUS: TAMAN STADION UTAMA, LANGENHARJO, GAJAH MADA)
Bibliographic record
Abstract
Green Open Space is an urban expanse covered by some of the trees as a shade area of the city and as the fulfillment of the needs public spaces for the community in activities and social. Efforts continue to be undertaken by the Green Open Space embodiment of Government, one that is by pioneering the implementation of ‘Program Pengembangan Kota Hijau’(P2KH) in improving the quantity and quality of Green Open Space in the area of the county or city. One form of Green Open Space i.e. the garden city that serves to improve the quality of urban and support the needs of the community in getting space to relax and leisure. The condition of children in district Kendal based on information from the community a lot of damage to either the environment or the facilities therein. This study aims to evaluate the application of Green Open Space on quality Grounds in the ‘Program Pengembangan Kota Hijau’(P2KH) sub district of Kendal which consists of the Stadion Utama, Langenharjo and Gajah Mada Parks. The method used was qualitative with deductive approach to rationalistic Unitarians. The analysis used a descriptive analysis i.e. qualitative and verification. Results from the study found that: 1) implementation evaluation results of Green Open Space in the gardens of the town Kendal not optimal, particularly on the Stadion Utama and Langenharjo Parks is still passive because it has not been supported with supporting facilities ; 2) evaluation results applying the Green Community is not optimal because there hasn't been an active ongoing activities so that it can not realize the active role of the community as a community in realizing the green city in district of Kendal; 3) factors that influence the application of optimal yet Green Open Space that is the location of the parks are not on the main road, the spread of vegetation are not optimal in improving microclimate because the settings are less noticed aspects of the function and benefits, passive Parks conditions due to lack of support facilities and the lack of appeal on the parks because the Setup and the pattern of plants that don't meet aesthetic; 4) factors that influence has not been optimal application of the Green Community that is constrained funds and lack of public awareness in maintaining and safeguarding the environment.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".