PENINGKATAN PENDAPATAN UKM MELALUI PENGUASAAN TEKNOLOGI PAKAN LELE DAN PEMANFAATAN LIMBAH LOKAL DESA JATISARI KECAMATAN JATISRONO, KABUPATEN WONOGIRI
Bibliographic record
Abstract
IbM activities carried out in the village Jatisari, local village government has formed groups of fish farmers, but due to constraints in the high feed costs, the number of farmers began to decrease from 20 to less than 5 people catfish farmers. Constraints, high feed costs caused by dependence on the feed manufacturers and fish farmers group is limited knowledge about the creation of alternative feed. Limitations in managing the business also experienced group of fish farmers, especially in production planning and financial of the business, through the program IbM, expected these problems can be finished.The IbM program consist of:(1)business management training (business plan), (2) training of feed manufacturing catfish with the use of local waste and can be as alternative feed, (3) training on mastery of engine technology prill fish pellets simple and integrated with dryers that can be used as an alternative to the rainy season. Outcomes of this program can be formed business group catfish farmers are independent, able to manage the business properly, can make alternative feed and capable of implementing the technology.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.033 | 0.005 |
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".