PELATIHAN PERTANIAN TERPADU PADA KELOMPOK PETERNAK "ANDHINI JAYA MAKMUR" KELURAHAN PAMPANG, PALIYAN, GUNUNGKIDUL
Bibliographic record
Abstract
Kondisi lahan pertanian di Kelurahan Pampang, Kapanewon Paliyan, Kabupaten Gunungkidul, Provinsi Daerah Istimewa Yogyakarta (DIY) lebih dari 95% berupa lahan kering, sehingga sangat tergantung pada ketersediaan air hujan. Tanaman padi gogo dan palawija hanya dapat ditanam sekali setahun. Budidaya sapi potong menjadi pilihan untuk meningkatkan pendapatan petani, karena nilai sapi sebagai modal/aset usaha juga lebih besar dibanding ternak lain seperti kambing maupun domba. Namun, petani peternak belum menerapkan secara penuh sistem pertanian terpadu dengan memadukan antara tanaman pangan dengan peternakan, dimana limbah tanaman pangan yang dapat dipakai sebagai pakan ternak dan kotoran ternak bisa untuk pupuk tanaman pangan. Kegiatan pengabdian ini bertujuan memberikan bimbingan teknis untuk meningkatkan IPTEKS dalam manajemen pemeliharaan sapi potong dan sistem pertanian terpadu. Kegiatan pertama berupa pemberian materi pertanian terpadu dan manajemen pemeliharaan sapi potong. Kegiatan kedua adalah pelatihan pengolahan pakan ternak berupa jerami amoniasi dan jerami fermentasi. Kegiatan ketiga adalah pelatihan pembuatan pupuk organik dari kotoran ternak untuk diaplikasikan di lahan pertanian. Kegiatan pengabdian ini dapat meningkatkan pengetahuan dan keterampilan petani peternak sehingga dapat menerapkan sistem pertanian terpadu pemeliharaan sapi potong dengan baik.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.037 | 0.008 |
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".