Evaluasi Lahan Berdasarkan Kualitas dan Karakteristik Lahan pada Bekas Pertambangan Tanah Urug di Dusun Pucang Gading, Kelurahan Hargomulyo, Kapanewon Kokap, Kabupaten Kulon Progo, Daerah Istimewa Yogyakarta
Bibliographic record
Abstract
Indonesia memiliki banyak aktivitas pertambangan, salah satunya kegiatan pertambangan tanah urug yang berlangsung diDusun Pucang Gading, Kelurahan Hargomulyo, Kapanewon Kokap, Kabupaten Kulon Progo, Daerah Istimewa Yogyakarta.Aktivitas pertambangan membuat lahan menjadi terdegradasi. Tujuan dari penelitian ini yaitu mengetahui kualitas dankarakteristik lahan berdasarkan kesesuaian lahan untuk arahan teknis reklamasi pertambangan sebagai pertanian lahan keringtanaman sengon dan ketela pohon. Metode yang digunakan adalah (1) survei dan pemetaan (2) Purposive Sampling (3)analisis laboratorium (4) weight factor matching. Parameter (karakteristik lahan) yang diamati pada lapangan yaitutemperatur(t) (rerata temperatur tahunan), ketersediaan air(w) (bulan kering, hujan pertahun), media perkaraan(r) (drainasetanah, tekstur tanah dan kedalaman efektif), retensi hara(f) (PH, H2O, KTK tanah, C-Organik), hara tersedia(n) (N Total, P2O5,K2O5), penyiapan lahan(p) (batuan permukaan, singkapan batuan), tingkat bahaya erosi(e) (bahaya erosi, lereng). Berdasarkanhasil dari evaluasi kesesuaian lahan tanaman sengon didapatkan 3 kelas, sedangkan tanaman ketela pohon didapatkan 2kelas. Rekayasa yang dilakukan untuk memperbaiki lahan adalah rekayasa teknik dengan pembuatan teras jenjang,pembuatan saluran irigasi dan revegetasi. Upaya perbaikan lahan yang dilakukan diharapkan membuat lahan kembalimenjadi produktif.Kata Kunci: Kualitas lahan; Karakteristik Lahan; Evaluasi Lahan
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".