Bibliographic record
Abstract
Even though they are separated by thousands of miles and have their own unique set of circumstances, the largest coal washing plants share some striking similarities. Most of these were originally designed as large preparation plants and then expanded upon success to become even larger. Although cultural differences distinguish each country, the operating costs do not. These facts and more were exposed during a CoalPrep 2001 technical season, titled 'World's Largest Prep Plants', where plant managers from the largest plants in four countries were invited to address their successes and highlight areas where they could improve. The session included: Zhang Jinxi, plant manager from the Pingshu An Tai Bao processing plant in China; Dan Yanchak, superintendent of CONSOL Energy's Bailey Central prep plant in Pennsylvania; Terry Fredin, superintendent of processing for Fording Coal's Fording River prep plant in British Columbia, Canada; and AJ van der Walt, chief engineer - process development for the Grootegeluk washery in the Republic of South Africa's (RSA) northern province. This article describes each of these plants. 3 figs., 1 photo.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.181 | 0.038 |
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".