Peningkatan Usaha Melalui Hilirisasi Produk Berbasis Tomat Pada Masyarakat
Bibliographic record
Abstract
Tanaman tomat mayoritas ditanam di Nagari Alahan Panjang, Kecamatan Lembah Gumanti, Kabupaten Solok. Masalah utama produksi tomat pada musim panen adalah rendahnya harga tomat sehingga banyak tomat yang dibiarkan membusuk pada batangnya dan tidak memiliki nilai ekonomis. Kegiatan hilirisasi produk berbahan dasar tomat bertujuan untuk meningkatkan usaha masyarakat, sehingga meningkatkan pendapatan dan kesejahteraan petani tomat di Alahan Panjang. Untuk mencapai tujuan tersebut, (1) dilakukan penyuluhan dan diskusi kelompok terarah tentang diversifikasi produksi tomat dengan hilirisasi produk berbahan dasar tomat. (2) Merancang produk berbasis tomat yang dapat dipasarkan (mutu, label dan kemasan). (3) Pengembangan keterampilan produk berbahan dasar tomat yang berdaya saing (menjaga higienitas sanitasi). Konseling, diskusi kelompok fokus, pelatihan, dan evaluasi adalah beberapa metode yang digunakan. FGD, penyuluhan, desain dan pencetakan label dan kemasan, pelatihan keterampilan membuat produk berkualitas sesuai higiene dan sanitasi, serta pendaftaran PIRT tomat yang terdiri dari dodol tomat, selai, dan saos merupakan hasil dari program pengabdian masyarakat ini. Kemampuan tersebut memicu keinginan peserta pelatihan untuk mengembangkan usahanya, sehingga meningkatkan pendapatan dan kesejahteraan masyarakat Alahan Panjang.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.054 | 0.021 |
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".