Pengkajian Pengembangan Model Pabrikasi Pupuk Organik: Studi Kasus di Kota Tasikmalaya, Jawa Barat
Bibliographic record
Abstract
Di beberapa lokasi di Jawa Barat telah diintroduksikan model pabrikasi pupuk organik skala pedesaan antara lain di Kecamatan Tamansari, Kota Tasikmalaya. Dalam pengembangan model tersebut masih menemui beberapa hambatan, yaitu permasalahan dalam pengembangan kelembagaan produksi pupuk organik, serta terkait dengan prilaku petani (pengguna). Pengkajian bertujuan 1) Mengevaluasi tingkat kelayakan usaha pabrikasi pupuk organik di pedesaan; 2) Mengetahui faktor penentu pengembangan produksi pupuk organik di pedesaan; 3). Mengetahui faktor yang mempengaruhi perilaku petani menggunakan pupuk. Pengumpulan data dilakukan melalui Expert Meeting dan survei wawancara terhadap 42 petani responden. Data diolah secara deskriptif, analisis Margin Benefit Cost Ratio, dan analisis regresi logistik binari. Hasil pengkajian menunjukkan bahwa usaha pabrikasi pupuk organik skala pedesaan dengan model introduksi mempunyai kelayakan usaha yang lebih tinggi, sehingga layak dikembangkan. Permasalahan utama pabrik pupuk organik pedesaan yaitu tingkat produksinya masih di bawah kapasitas produksi optimum disebabkan pemasaran hasil yang kurang baik. Peluang petani menggunakan pupuk organik dipengaruhi oleh beberapa faktor yaitu proporsi tanaman semusim yang diusahakan, kepemilikan ternak, serta keterampilan dalam membuat pupuk kompos terutama dalam pengetahuan penggunaan dekomposer.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".