PENGEMBANGAN PETERNAKAN BERSIH DI DESA NGUNUT KECAMATAN JUMANTONO KABUPATEN KARANGANYAR
Bibliographic record
Abstract
In the Pemitra community service activities for development cleaner farming in Desa Ngunut Kecamatan Jumantono kabupaten Karanganyar, has been done for directing activities, training, and mentoring for farmers of cattle and chickens in Desa Ngunut, Jumantono, Karanganyar. In this Pemitra activities have been conductedbriefings and training on producing of liquid and solid organic fertilizer, planting cassava and sengon by utilizing manure, the introduction of biogas technology applications on cattle ranchers. The results that have been obtained from this Pemitra community activities that partners have been able to understand and have the skills for managing livestock clean the biogas technology applications, the use of probiotics or fermentator in farm management, made of solid and liquid organic fertilizer, as well as the use of organic fertilizer for agricultural development cassava and sengon. The constraints and problems had been faced by the partners were 1) lack of farmer groups that have organizational unity in designing, managing, implementing and evaluating the work program. So that the unity and continuity in performing community service activities can not be done well, 2) Most of the participants were active inpemitra was the village officials and their family, so not much give a breadth of benefits to the general public.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.080 | 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".