PENINGKATAN POPULASI DAN PEMANFAATAN AYAM LOKAL BENGKULU MELALUI PENERAPAN TEKNOLOGI MIKRONUTRISI DAN PENETASAN SEDERHANA UNTUK PENINGKATAN PENDAPATAN MASYARAKAT
Bibliographic record
Abstract
The program aims to increase the community interest to local genetic wealth of Burgochicken. The long term goal of this training is to make the village Sumber Rejo as centersof production, marketing and research activities of Burgo chicken. The method used in thisservice activity is a demonstration plot. The group is the group members who have beenprovided the knowledge and skills regarding the operation of the hatching machine. In theaspect of feed, farmers are given knowledge about the type of food, kathuk leaf supplementand ration formulation and feed mixing practices. This method is applied to support theacceleration of population growth of Burgo chicken. Extension and practice of usinghatching machine, as well as kathuk leaf extract supplementation, also introduce to targetfarmer groups as a holistic effort in increasing Burgo chicken population. Theimplementation of this activity shows some facts that (1) Burgo chicken is relatively hardto find and can be sold at high prices. (2) During the culture treatment of feed that hasbeen tested in previous studies, the production of eggs produced relatively high at anaverage of 48 eggs per head per egg-laying period, with low egg weight at an average of35 grams per egg. (3) Power of hatching eggs based on observations is still in the goodrange at 41 %. (4) The DOC mortality rate reaches 50 %. Egg production data showsgood potential. It can be seen from the total eggs produced within three months of culture(48 eggs) from five Burgo hens. High mortality and unmaximally hatchability will beincreased along with the increasing skill in the culture farming and operation of thehatching machine.Key words: Burgo Chicken, Hatching, Katuk Leaf Extract, Revenue
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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 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.032 | 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".