An Empirical Study on Improving Quality of Coal-Mining Refuse for Re-Vegetation Using Amendments
Bibliographic record
Abstract
Re-vegetation on closed mining-sites for carbon sequestration and/or bio-energy production is one of the strategies of addressing the world-wide issues of energy crisis and global warming. However, mine soils including coal-mining refuse usually have poor quality and are unfavorable to plant growth. Thus, the major objective of this study was to improve quality of coal-mining refuse under laboratory conditions using zeolite, flue gas desulfurization gypsum (FGD), flyash, and biosolids at 10% (w/w) rate. Chemical analysis did not indicate any significantly high concentrations of toxins in the solid or the solution phase, suggesting that soil acidity was the principal chemical constraint hindering re-vegetation. In this context, FGD was the best among the tested materials for increasing soil pH and improving lettuce (Lactuca sativa) seed germination, while application of biosolids significantly enhanced soil aggregate stability. Specifically, laboratory tests showed that application of FGD increased pH of the acidic coal refuse samples from initial 3.80-4.66 to 5.70-6.60 and enhanced the growth of germinated lettuce seedlings in mine soil solution from 2.9-4.4 cm to 5.9-8.6 cm. The biosolids amendment increased the geometric mean diameter of the mine soil aggregates from the antecedent 0.93-0.99 mm to 1.13-1.25 mm. However, use of zeolite and fly-ash did not significantly improve the soil quality.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".