Identifikasi Lapisan Batubara dari Segi Geokimia Anorganik
Bibliographic record
Abstract
Sari. Penentuan kelimpahan unsur kimia dalam percontoh lapisan batubara dilakukan melalui analisis unsur kimia pada abu batubara berupa V, Ni, Cr, Co, Mo, Cu, Zn,Pb, Mn, Sn, Sr, Ba, Cd, As, Ag, Al, Ca, Mg, Fe, Na, dan K serta melalui fraksi-fraksi batubara hasil pemisahan sink and float berupa Si, Al, Ca, Mg, Fe, Ni, Cu, Na, dan K. Jenis unsur tersebut dapat menjadi parameter identifikasi lapisan yang berguna dalam korelasi stratigrafi lapisan, dan gambaran kecenderungan asosiasi mineral dengan golongan maseral batubara yang dapat mendukung pararameter tadi. Dari analisis principal component didapati bahwa Ni, Cr, dan V merupakan parameter yang baik untuk identifikasi lapisan; sebagai pembanding digunakan data lapisan batubara Bihar dari India dan Kanada. Unsur ini diperkirakan terikat pada molekul bahan organik sebelum diagenesis gambut, bersama terbentuknya kuarsa, kaolinit, gips dan cenderung berasosiasi dengan huminit/vitrinit. Mineral yang cenderung terbentuk selama diagenesis adalah siderit, kalsit, Mnkarbonat, serta illit/smektit yang lebih berasosiasi dengan inertinit. Coal Seam Identification from the Aspect of Inorganic Geochemistry Abstract. Elements abundance in the coal scams samples were determined by analyzing 1) the coal ash, namely V, Ni, Cr, Co, Mo, Cu, Zn, Pb, Mn, Sn, Sr, Ba, Cd, As, Ag, Al, Ca, Mg, Fe, Na, and K, and 2) maceral rich coal fractions by sink & float separation, namely Si, Al, Ca, Mg, Fe, Ni, Cu, Na, and K. The scope is to obtain certain elements as a parameter for coal scam identification which could be applied as a useful stratigraphic correlation tool and an inferrence on mineral occurence associated with maceral groups within the coal seam Ni, Cr, and V are shown to be the best parameter for seam identification by means of principal component analysis; for comparison purposes data from Bihar (India) and Canada were used. These elements are interpretated to be fixed to the organic molecules before peat diagenesis contemporancously with the formation of quartz, kaolinite, and gypsum and tend to associate with huminite/vitrinite. Minerals tend to be formed during diagenesis are siderite, calcite, carbonate of Mn, and illite/smectite with the associated inertinite.
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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 | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".