Sustainability Analysis of Intensive Duck Farming System in Sliyeg District, Indonesia: MDS and MICMAC Analysis Approach
Bibliographic record
Abstract
This study aimed to calculate a sustainability index of duck farming.Data was collected through a questionnaire by a scientific judgment of expert researchers in duck farming.Data were grouped using the multidimensional scaling (MDS and MICMAC and analyzed using) comprising social, economic, ecological, technological, and human resources dimensions with a total of 38 attributes in Rapfish software.The sustainability index was calculated as 44.13%, which indicates that farming has a less sustainable category with consideration of some leverage factors.These findings indicate that the high feed price, fluctuations in the price of duck products, diseases, and an extensive maintenance system warrant further attention to improve sustainability.MICMAC's analysis showed that the intensity of counseling, knowledge of livestock health, and livestock waste management are the main driving variables and prerequisites in determining the sustainability of a duck business.The prospective analysis identified several strategies to improve sustainability that can be carried out, including increasing the capacity of human resources through training/counseling/comparative studies, increasing the capacity of existing institutions, and advocating and socializing intensive duck business cultivation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".