Pendampingan Usaha UMKM Deppa Tori’ : Optimalisasi Produksi dan Keuangan UMKM Deppa Tori’ sebagai Ikon Kuliner untuk Mendukung Pariwisata Lokal Toraja
Bibliographic record
Abstract
This community service program aims to assist Deppa Tori’ business actors in optimizing production and financial management in order to improve the quality, capacity, and sustainability of their business. The methods used in this program include training, direct assistance, and implementation of more efficient production strategies and more systematic financial records. In addition, this program also prioritizes branding and marketing aspects that are in line with local tourism potential, so that Deppa Tori’ can be better known as a typical souvenir that supports the tourism industry in the Toraja area. The expected results of this activity are increased production efficiency, better product quality standards, and a more organized financial system for Deppa Tori’ business actors. Thus, MSMEs can develop more professionally and contribute to strengthening the local creative economy and tourism sectors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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; both teacher heads agree on what is shown here.
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".