Profil Desa Wisata Samiran di Lereng Merbabu-Merapi Kecamatan Selo Kabupaten Boyolali Propinsi Jawa Tengah
Bibliographic record
Abstract
Desa Wisata Samiran sudah dirintis sejak tahun 2006, berada di dataran tinggi lereng Merbabu-Merapi atau di lembah Gunung Merbabu-Merapi memiliki panorama alam yang indah dan udara sejuk. Secara administrasi berada diwilayah Kecamatan Selo Kabupaten Boyolali Propinsi Jawa Tengah, mempunyai luas 663,329 ha dengan elevasi 1.400-2.550 m dpl, jenis tanah andosol/andisols (lereng Merbabu) dan regosol/entisols (lereng Merapi). Dataran tinggi dengan tanah andosol secara agroklimat cocok/sesuai untuk pengembangan beraneka agrowisata, yaitu : tanaman perkebunan (teh dan kopi Arabika), tanaman buah (tledung, apel dan jeruk), tanaman hias, serta sayuran. Kegiatan pengabdian ini bertujuan untuk perintisan pendampingan pada kelompok tani di Desa Wisata Samiran. Jenis kegiatan yang dilakukan adalah pengumpulan data/informasi yang berkaitan dengan “Profil Desa Wisata Samiran”, yaitu meliputi : (1) pengumpulan data/informasi kondisi agroklimat dan jenis usahatani; (2) pengumpulan data/informasi keberadaan dan kegiatan kelompok tani; serta (3) pengumpulan data/informasi keberadaan obyek wisata di wilayah Desa Wisata Samiran. Target dari kegiatan pengabdian ini diperoleh Profil Desa Wisata Samiran yang nantinya dapat digunakan sebagai data dasar untuk pengembangan agrowisata di Desa Wisata Samiran, khususnya sebagai peningkatan atensi akademisi program pendampingan/pengabdian masyarakat dari Grup Riset Pengelolaan DAS dan Agroekosistem, Fakultas Pertanian Universitas Sebelas Maret, serta para pihak yang berminat.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".