Pengaruh Kemiringan Lereng terhadap Nilai Laju Erosi di PT Darma Henwa Bengalon Coal Project
Bibliographic record
Abstract
PT Darma Henwa melakukan kegiatan reklamasi pada lahan bekas tambang sebagai salah satupenanggulangannya, namun tidak menutup kemungkinan tetap terjadi erosi pada lahan reklamasi. Tujuan daripenelitian untuk mengetahui besar laju erosi berdasarkan kemiringan lereng datar, miring, dan curam,mengetahui faktor pengaruh dari curah hujan dan vegetasi terhadap erosi. Metode untuk penentuan titikpengukuran erosi dan pengambilan sampel tanah adalah purposive sampling. Pengukuran erosi menggunakanmetode tongkat dengan menghitung penurunan tanah pada tongkat. Melakukan analisis statistik korelasi pearson.Lahan reklamasi dengan kemiringan lereng datar didapatkan nilai erosi sebesar 66,86 ton/ha/thn, kemiringanlereng miring sebesar 46,11 ton/ha/thn, dan kemiringan lereng curam sebesar 32,29 ton/ha/thn. Hasil tersebutmenunjukan erosi yang dihasilkan tidak signifikan berpengaruh oleh faktor hujan pada kemiringan lerengtertentu, melainkan adanya pengaruh dari faktor lain yaitu vegetasi. Pada kemiringan lereng curam dan miringmemiliki nilai coverage yang lebih tinggi sebesar 236,80 m2 dan 115,56 m2 dibandingkan nilai coverage padakemiringan lereng datar sebesar 41,02 m2, hal ini menyebabkan erosi pada kemiringan lereng datar lebih besar.Hasil analisis korelasi antara intensitas hujan dengan nilai erosi termasuk kedalam klasifikasi sangat kuat, nilai Rpada kemiringan lereng datar sebesar 0,9181, kemiringan lereng miring sebesar 0,9118 dan kemiringan lerengcuram sebesar 0,9106.Kata Kunci: Erosi; Tongkat; Kemiringan Lereng; Lahan Reklamasi; Vegetasi
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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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".