PROSPEK ENDAPAN KROMIT PADA LATERIT DOSAY, PEGUNUNGAN CYCLOOP, SENTANI BARAT, JAYAPURA, PAPUA
Bibliographic record
Abstract
Keterdapatan mineral kromit di Wilayah Dosay, Kecamatan Sentani Barat,Provinsi Papua, merupakan temuan baru yang menarik untuk di kaji secara lebih jauh.Adanya indikasi tersebut telah memberikan gambaran kondisi mineralisasi yang belum terungkap oleh penyelidik sebelumnya, terutama terutama berkaitan dengan keterdapatan kromit di wilayah ini. Secara geologi Jalur Pegunungan Cycloop dimana indikasi kromit ditemukan memiliki ciri yang karakteristik. Kromit ditemukan dalam batuan ultrabasa kelompok ofiolit Pegunungan Cycloop. Batuan ofiolit merupakan batuan induk (host rock) dari kelompok mineral logam jenis kobalt (Co), nikel (Ni), besi laterit (Fe), platinum (Pt), Paladium (Pd) dan kromit (Cr).Dari hasil penyelidikan oleh Pusat Sumber Daya Mineral, Batubara dan Panas Bumi, Badan Geologi th 2016, di daerah Sentani Barat diketahui anomali kromit ditemukan cukup signifikan yaitu dari kisara 1,3% hingga 4,7% dari soil hingga lapukan batuannya (saphrolit). Anomali ini mencapai grade tertinggi sekitar 130 kali lipat kelimpahan unsur kromit di kerak bumi. Sementara dari kelompok mineral logam lainnya seperti kobal, nikel dan besi kehadirannya tidak begitu signifikan.Adanya indikasi kromit yang kuat di horizon soil bagian atas hingga saphrolit pada bagian bawah diharapkan akan menjadi suatu temuan baru yang menarik dan berharga baik secara “scientific”c maupun ekonomi diwilayah ini dimasa mendatang
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 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".