PENGEMBANGAN DAN PENINGKATAN NILAI TAMBAH BUDIDAYA JAMUR TIRAM MELALUI PELATIHAN SOSIS JAMUR KEPADA KELOMPOK WANITA MANDIRI DESA GERDU
Bibliographic record
Abstract
ABSTRAKProduksi jamur tiram cukup melimpah di Kabupaten Karanganyar termasuk di Desa Gerdu Kecamatan Karangpandan yang menjadi desa mitra Tim Pengabdian. Nilai jual jamur tiram dapat ditingkatkan dengan membuat produk makanan berbahan dasar jamur tiram, seperti : kripik jamur tiram, sate jamur, stick jamur, serundeng jamur dan produk jamur lain yang mempunyai nilai tambah. Kelompok Wanita Mandiri Desa Gerdu mencari alternatif produk berbahan dasar jamur tiram yang dianggap mampu diproduksi dan bisa meningkatkan tambahan pendapatan dari para anggota kelompok. Sosis Jamur menjadi pilihan produk yang akan dikembangkan oleh mitra dengan alasan : 1) Produk sosis jamur masih sangat jarang di jual di pasar, 2) Diharapkan sosis jamur bisa menjadi alternatif pengganti sosis sapi dan ayam yang sudah terlebih dahulu populer di masyarakat. 3) Sosis Jamur diharapkan menjadi produk unik yang dijadikan oleh-oleh bagi wisatawan yang berkunjung ke destinasi wisata di kabupaten Karanganyar. Untuk pencapaian tujuan tersebut maka Tim Pengabdian melakukan pelatihan dan pendampingan bagi mitra berupa : pelatihan pembuatan produk sosis jamur tiram Kata Kunci : Jamur Tiram, produk sosis jamur
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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 | 0.001 | 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 | 0.002 | 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) | 0.024 | 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".