The use of the Roben Jig for preparation of clean coal samples of Western Canadian coals via density separation
Bibliographic record
Abstract
This study compared a water-based and a solvent-based method for removing ash (washing) from coal: jigging coal in water using the Roben Jig and the float/sink method using conventional organic liquids (naphtha, perchloroethylene, methylene bromide), respectively. Clean coal curves from the two processes were compared for six coal types from British Columbia, Canada. The clean coal curve for the Roben Jig deviated from that of the organic liquids when the near-density material content was high. Also, particles were misplaced within the jigging column; however, a simple “rejig” process was capable of further cleaning the coal. The Roben Jig was used to create a clean coal sample of at least 400 kg by washing coal in batches. The clean coal curves for the jig were similar. Minor differences could be attributed to the occurrence of misplaced particles. Although the Roben Jig does not provide perfect separation of coal based on density for use in wash plant design studies, previous work has established that it is capable of creating representative clean coal composites without the use of organic liquids.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".