Tata Ruang Pengembangan Ternak Kerbau Sebagai Penghasil Daging dalam Menunjang Swasembada Daging, di Kabupaten Pasaman Timur Sumatera Barat
Bibliographic record
Abstract
This research was conducted in the district of Pasaman, West Sumatra, with the aims were: (a) to analyze the deployment center area (pattern space) buffaloes; (b) to analyze the spread of the development area (space structures) business buffaloes; and (c) to analyze the management of maintenance buffaloes in business center area.The study was conducted in two stages of analysis;The first step was to analyze the geographical distribution centers and areas of business development buffaloes in the district of Pasaman, using secondary data.Research on phase two was survey method and observations on the territory of the business center buffaloes to analyze the management of maintenance, using a questionnaire.The results showed that the business center area buffaloes in Pasaman regency consists of the District Rao Utara, Tigo Nafari, and Bonjol.Areas that have the potential for development of buffaloes based on availability of land that are subdistrict Panti, Duo Koto, Rao Selatan and districts Rao, the based supporting facilities are the districts Tigo Nagari, Lubuak Sikapiang, and districts Rao.The buffaloes kept are swamp buffaloes which is feed grass field, only few of farmer feeding concentrate, raising system is semi-intensive; prevention/ treatment of diseases is conducted by sanitation; Most of the marketing of buffalo is still through collectors.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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; 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".