Relationships Between Chemical and Mineral Composition of Coal and Their Potential Applications As Genetic Indicators.\nPart 1. Chemical Characteristics
Bibliographic record
Abstract
The relationships between the chemical and mineral composition of 37 coal samples from Australia, Bulgaria, USA, Japan, Canada, South Africa, China, Spain, and Ukraine, which differ considerably in their geology, rank, age, ash yield, chemistry and mineralogy, have been investigated. For that purpose complete data from chemical (proximate, ultimate and ash analyses) and mineral composition (major and minor minerals) of these samples have been used. The study explains initially some general considerations of the inorganic matter in coal. Then, the work provides and elucidates the statistically significant positive or negative correlations of chemical characteristics of the coals studied. Further, different geochemical indicators for some genetic interpretations of coal formation are also provided and described. The correlations of minerals in coals, as well as the potential applications of relationships among chemical and mineral composition are described in Part 2 of the present work.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".