Assessing the technological value of coal in coking
Bibliographic record
Abstract
Coking coal of the same rank from different countries and fields may be distinguished in terms of use value by rating on the basis of seven technological and petrographic characteristics that determine the coke yield and properties: the ash content A d ; the total sulfur content S t d ; the yield of volatiles V daf ; the plastic-layer thickness y; the vitrinite reflection coefficient R o; the content of vitrinite-group macerals Vt; and the basicity index B b. A range of values and a rating (on a scale from 1 to 10) are established for each of these parameters. Each rating corresponds to a particular score (from 0.1 to 1.0). Ranges of A d , S t d , Vt, and B b are established for the whole metamorphic series, while ranges of V daf , y, and R o are established for individual ranks and groups of ranks. Altogether, 105 coking coals from Ukraine, Russia, the United States, Australia, and Canada that are used at Ukrainian coke plants are investigated. The range of rating scores and their mean values are determined for individual coal ranks and groups. As an example, three bituminous coals from Ukraine, the United States, and Australia are compared by the proposed method. This method permits objective assessment of the technological value of coal within a single rank and the selection of the best purchase option.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".