Evaluasi Tingkat Keberhasilan Reklamasi Pascatambang Batugamping di Quarry A PT. Semen Tonasa, Kecamatan Bungoro, Kabupaten Pangkep, Provinsi Sulawesi Selatan
Bibliographic record
Abstract
PT. Semen Tonasa merupakan salah satu perusahaan yang bergerak di bidang pertambangan Batugamping di Indonesia Timur yang terletak di Desa Biringere, Kecamatan Bungoro, Kabupaten Pangkep, Provinsi Sulawesi Selatan. Penambangan yang diterapkan di PT. Semen Tonasa adalah sistem tambang terbuka atau metode quarry. PT. Semen Tonasa melakukan kegiatan reklamasi di quarry A. Di reklamasi tersebut terdapat beberapa blok. Di blok tersebut mempunyai usia dan vegetasi yang berbeda. Namun, ada permasalah yang terjadi di lokasi penelitian yaitu dengan ditemukan adanya erosi dan juga tanaman yang kurang sehat, sehingga penilaian keberhasilan reklamasi belum mencapai 100% sehingga perlu dilakukan evaluasi penilaian reklamasi. Tujuan dari penelitian ini yaitu untuk mengetahui persentase tingkat keberhasilan reklamasi dan mengetahui rekomendasi arahan pengelolaan reklamasi. Metode yang digunakan adalah (1) survei dan pemetaan (2) evaluasi (3) skoring (4) Analisis. Evaluasi tingkat keberhasilan dilakukan dengan tiga parameter yaitu penatagunaan lahan, revegetasi, dan penyelesaian akhir yang berpedoman pada KEPMEN ESDM Nomor 1827 K/30/MEM/2018. Berdasarkan hasil evaluasi tingkat keberhasilan didapatkan sebesar 91,67% yang tergolong baik. Kata Kunci: Batu gamping, quarry, reklamasi, tata guna lahan, revegetasi
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".