Prosedur dalam Penerapan Tertib Pemeliharaan Tanah dan Lingkungan Hidup pada Pertambangan Batu Gamping Semen A di Banyumas
Bibliographic record
Abstract
Catur tertib pertanahan pada dasarnya dilaksanakan dengan tujuan mengimplementasikan Undang-Undang Pokok Agraria untuk mengembangkan perlindungan hak-hak atas tanah serta penggunaannya. Hal ini berbanding terbalik dengan kondisi yang ada di tambang batu gamping Semen A, di mana terdapat lubang galian tambang yang berdampak terhadap lingkungan. Penambangan ini menimbulkan permasalahan lingkungan yang signifikan, seperti debu akibat angin kencang, peningkatan suhu udara, kebisingan dari alat berat, serta kekeringan sumber air sejak adanya tambang. Bertolak dari masalah tersebut, penelitian ini bertujuan untuk menyusun prosedur penerapan catur tertib pertanahan keempat, yaitu tertib pemeliharaan tanah dan lingkungan hidup pada pertambangan batu gamping di Tambang Semen A. Metode pada penelitian ini adalah studi kualitatif menggunakan Soft System Methodology (SSM) dengan alat bantu Analytical Hierarchy Process (AHP). Data yang digunakan meliputi data primer berupa hasil observasi, wawancara, kuesioner, dan dokumentasi, serta data sekunder berupa laporan peneliti terdahulu, studi literatur, dan telaah regulasi. Hasil penelitian ini adalah prosedur dalam penerapan catur tertib pertanahan keempat, yaitu tertib pemeliharaan tanah dan lingkungan hidup pada pertambangan batu gamping di Tambang Semen A.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.003 |
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".