PEMANFAATAN TEKNOLOGI PENGELOLAAN OPT TANAMAN SAYURAN BERBAHAN BAKU RAMAH LINGKUNGAN DI KANAGARIAN LASI KECAMATAN CANDUANG KABUPATEN AGAM
Bibliographic record
Abstract
Kegiatan tentang pemanfaatan teknologi pengelolaan organisme penganggu tanaman (OPT) pada tanaman sayuran berbahan baku ramah lingkungan telah dilakukan di Kelompok Tani Mitra di Nagari Lasi Kecamatan Canduang Kabupaten Agam pada bulan September hingga November 2016. Kegiatan ini bertujuan untuk meningkatkan pengetahuan petani tentang serangan OPT sayuran dan bagaimana mengelola OPT menggunakan biopestisida dan pestisida botani. Kegiatan ini meliputi: pemantauan tingkat serangan OPT di lahan mitra, penyuluhan, pelatihan, dan evaluasi hasil kegiatan. Hasil pemantauan tingkat serangan OPT pada tanaman sayuran mitra berkisar antara 18 - 27% untuk penyakit dan 15 - 20% untuk hama. Dari kegiatan dapat dikesimpulkaan yaitu: (1) tingkat OPT pada tanaman sayuran mitra cukup tinggi, (2) Petani belum memahami OPT yang menyerang tanaman sayuran mereka, (3) Petani tidak memahami metode pengelolaan OPT menggunakan biopestisida dan pestisida botani, dan (4) penyuluhan dan praktek lapangan, telah meningkatkan pengetahuan petani tentang pengelolaan OPT sayuran dengan menggunakan biopestisida dan pestisida botani.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".