Evaluation of coal component liberation upon impact breakage by MLA
Bibliographic record
Abstract
To elucidate the coal component liberation mechanisms upon typical impact breakage, the coal was selected from the Shangwan mine in China. Thereafter, mineral liberation analysis was adopted as the main method for investigating phase interface characteristics, classification characteristics of the various classes of comminuted products, and changes in the mineral area and length gradients upon mechanical impact breakage. The results indicate that the grain sizes of the comminuted products decreased in the following order: slag product >classifier product >bag product. Moreover, the disseminated grain size, dissemination mode, and original cleavages had a direct influence on the recovery rate of the completely liberated phase. The coal, which formed the main component of the samples, exhibited the highest cumulative mass recovery (CMR) in classified products and was most easily liberated. Furthermore, the kaolinite-bearing coal and pyrite were moderately liberated, the quartz and kaolinite were difficult to liberate, and the illite was extremely difficult to liberate. Under mechanical impact breakage, intragranular and intergranular fractures mainly occurred in particles, with the changes in the mineral boundary and mineral area corresponding to varying fracture modes. To a certain extent, the particle comminution could be quantified by the changes in the slope of the equivalent circle diameter–- CMR curve. It was determined that the main cause of the increased liberation degree is the generation of the completely liberated phase of the component of interest. Furthermore, by adopting the liberation factor for quantification of the mineral liberation, the calculation model for coal component liberation was obtained.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".