Analysis of Factors Causing Death of Pigs Due to African Swine Fever Outbreak in Toba District, North Sumatra Province
Bibliographic record
Abstract
Abstract This study aims to analyze the factors causing livestock death in pig farms due to African Swine Fever (ASF) outbreak in Toba District, North Sumatra Province. The research was conducted at a community pig farm in Toba District, North Sumatra Province. Data collection was carried out from May to July 2022. Data collection was carried out by interviewing farmers, as well as conducting direct observations in the pens of pig farms in Toba Regency. Sixty-five farmers were selected as respondents in this study. Factors causing the death of pigs were analyzed using logistic regression. The dependent variable used is pigs infected with ASF (dead pigs) with a value of 1, and a value of 0 if there are no pigs infected with ASF. Variables that have a significant influence on the number of deaths of pigs in smallholder farms in Toba Regency are: farmer’s education level, main occupation of the farmer, location of pens, distance of pens from other farmers’ pens, housing systems, sanitize goods that will enter the pen, use of disinfectants, origin of male or boar, type of feed given, feed processing before being given to pigs, and carrying out fumigation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".