POTENSI LAHAN UNTUK KOLAM IKAN DI KABUPATEN CIANJUR BERDASARKAN ANALISIS KESESUAIAN LAHAN MULTI KRITERIA
Bibliographic record
Abstract
Kabupaten Cianjur merupakan salah satu wilayah yang potensial untuk pengembangan budidaya ikan air tawar. Sampai saat ini, proporsi terbesar dari total produksi ikan berasal dari Keramba Jaring Apung (KJA) Waduk Cirata. Waduk Cirata saat ini sudah dan sedang mengalami penurunan kualitas sehingga mengurangi produksi ikan Kabupaten Cianjur. Oleh karena itu, diperlukan alternatif cara pemeliharaan ikan selain KJA. Salah satunya adalah kolam. Informasi mengenai wilayah yang berpotensi untuk lokasi budidaya ikan merupakan faktor penting dalam pengembangan perikanan. Penelitian ini bertujuan untuk memetakan tingkat kesesuaian lahan untuk kolam. Penentuan kesesuaian lahan dilakukan dengan aplikasi Sistem Informasi Geografis (SIG) dan Evaluasi Multi-kriteria (Multi Criteria Evaluation, MCE). Hasil analisis kesesuaian lahan menunjukkan lokasi yang sesuai untuk kolam seluas 86,511 ha (23.9% dari total luas wilayah). Hasil analisis terhadap lahan yang sesuai, lokasi yang tersedia seluas 74,062 ha (20.5%) dan yang tidak tersedia seluas 12,449 ha (3.44%). Hasil penelitian menunjukkan bahwa pengembangan lahan untuk kolam masih mungkin dilakukan di Kabupaten Cianju
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".