Quality Change of Mine Soils From Different Sources in Response to Amendments - A Laboratory Study
Bibliographic record
Abstract
The desired vegetation is often difficult to establish in mine soils without proper amendments. In this paper, two coal-mine soils were studied to assess changes of soil quality in response to a range of amendments. Sample A with relatively neutral pH (6.1) was obtained from the top soil at a reclaimed surface-coal-mining site while the other sample B (pH 4.4) from nearby piles of coarse coal refuse. Amendments included zeolite of two grain sizes, flue gas desulfurization gypsum (FGD), flyash, and biosolids at 10% (w/w) rates. Chemical analysis showed that neither soil contained significant amounts of toxic elements except for B and Sr. Lettuce seed germination indicated that original sample A possessed a better quality with 76.7% germination and 7.3 cm shoot length in comparison with soil B having 60% germination and 3.7 cm shoot length. FGD increased the pH of both soils from 6.1 to > 8.0 and from 4.4 to > 6.0, respectively. Moreover, FGD significantly enhanced lettuce germination (83.3%) and seedling growth (6.7 cm) in soil B but did not greatly affect those in soil A. We concluded that soil acidity might be the major chemical constraint inhibiting plant establishment in acidic mine soils and that amendments such as FGD which could increase soil pH up to the neutral level would enhance plant growth in these soils. Biosolids enhanced aggregate stability of both soils with geometric mean diameter increased from antecedent values of 0.90-0.97 mm to 1.2-1.6 mm. Zeolites or flyash did not significantly impact the mine 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".