Peran Manajemen Populasi Anjing dalam Pemberantasan Rabies: Studi Kasus di Desa Pejeng, Kecamatan Tampaksiring, Kabupaten Gianyar, Provinsi Bali
Bibliographic record
Abstract
Sejak tahun 2008 hingga saat ini rabies masih endemis di Bali. Manajemen Populasi Anjing adalah suatu upaya untuk menstabilkan populasi anjing yang terdiri dari enam komponen yaitu edukasi, legislasi, identifikasi dan registrasi, vaksinasi, sterilisasi, serta manajemen sampah dalam rangka pemberantasan rabies. Program ini telah dilaksanakan di Desa Pejeng, Kecamatan Tampaksiring, Kabupaten Gianyar, Provinsi Bali pada bulan Nopember 2016 yang didanai oleh Food and Agriculture Organization. Penelitian ini bertujuan untuk mengetahui perubahan pengetahuan, sikap dan cara pemeliharaan anjing pada masyarakat di Desa Pejeng setelah penerapan Manajemen Populasi Anjing. Penelitian observasional ini menggunakan cross sectional study melalui pengamatan dan penyebaran kuisioner langsung ke lapangan. Teknik pengambilan sampel menggunakan teknik Probability sampling, Proportional stratified sampling. Jumlah sampel yang diambil 313 responden. Penelitian dilaksanakan pada bulan Nopember 2016 (sebelum Manajemen Populasi Anjing diterapkan) dan pada bulan September 2018 setelah Manajemen Populasi Anjing diterapkan). Data yang terkumpul dianalisis secara deskriptif. Hasil sebelum dan sesudah penerapan Manajemen Populasi Anjing dianalisis dengan analisis non parametrik mengunakan uji Wilcoxon. Berdasarkan hasil penelitian, disimpulkan bahwa Penerapan Manajemen Populasi Anjing secara signifikan (P<0,05) dapat mengubah pengetahuan, sikap dan cara pemeliharaan anjing pada masyarakat di Desa Pejeng menjadi lebih baik.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".