Identifikasi Atraksi di Orchid Forest Cikole Kecamatan Lembang
Bibliographic record
Abstract
Abstract. The protective function greatly influences forests, especially protected forests, along with population growth and increasing development, it can also disrupt the protective function of forests, including protected forests. Orchid Forest Cikole is an eco-tourism-based tourist destination built in a protected forest area under the auspices of PERHUTANI. Orchid Forest Cikole has a land area of around 12.4 Ha and to date only around 8.8 Ha has been built. This research aims to examine the protective function in Orchid Forest Cikole, the variables in this research are build up area coverage, distribution flora and fauna, distribution of waste generation, water catchment coverage and use of clean water. The data collection method in this research uses primary and secondary data collection methods, where the primary data collection method is by conducting direct surveys and conducting interviews with existing relevant parties. in Orchid Forest Cikole and for the secondary data collection method by looking at the geographical location of satellite images on Google Earth. For the analysis method in this research, the spatial analysis method is used, where the output is a map of each variable. Abstrak. Fungsi lindung sangat berpengaruh terhadap hutan terutama bagi hutan lindung,seiring dengan pertambahan penduduk dan juga bertambahnya pembangunan juga dapat mengganggu fungsi lindung pada hutan-hutan diantaranya hutan lindung.Orchid Forest Cikole merupakan destinasi wisata yang berbasis ekoeduwisata yang dibangun dikawasan hutan lindung dibawah naungan PERHUTANI.Orchid Forest Cikole memiliki luas lahan sekitar 12,4 Ha dan sampai saat ini baru sekitar 8,8 Ha saja yang terbangun.Penelitian ini bertujuan untuk mengkaji fungsi lindung yang ada di Orchid Forest Cikole,variabel pada penelitian ini yaitu cakupan build up area, sebaran flora fauna, sebaran timbulan sampah, cakupan resapan air dan pemanfaatan air bersih.Metode pengumpulan data pada penelitian ini menggunakan metode pengumpulan data perimer dan sekunder,dimana untuk metode pengumpulan data perimer dengan cara melakukan survey langsung dan melakukan wawancara pada pihat terkait yang ada di Orchid Forest Cikole dan untuk metode pengumpulan data sekunder dengan cara melihat letak geografis dari citra satelit yang ada pada google earth.untuk metode analisis pada penelitian ini menggunakan metode analisis spatial yang dimana outputnya berupa peta dari masing-masing variabel
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".