PHYTOSTABLIZATION OF SULPHIDE MINE TAILINGS
Bibliographic record
Abstract
Orphaned or abandoned sulphide tailing disposal sites pose significant environmental hazards, including eolian dispersion, water erosion, acid mine drainage, and heavy metal mobility. Phytostabilization, an eco-friendly strategy, entails the use of alkaline amendments alongside non-native plant species capable of thriving in environments with high concentrations of heavy metals. A greenhouse experiment was conducted to assess the effect of a commercial cement which contained 46.3% sand, applied alone or combined with three magnesium (Mg) reagents on the shoot dry yield (DMY) of ryegrass (Lolium multiflorum Lam.) grown on sulphide mine tailings (SMT) (pH 3.0). The 29 treatments evaluated were replicated three times in a randomized complete block design. All pots received N-P-K fertilizer. Treatments combining cement and Mg reagents significantly increased the pH of the cultivated tailings. Magnesium oxide (MgO) and magnesium hydroxide (Mg(OH)?), when mixed with the cement, were more effective than magnesium carbonate (MgCO?) in maintaining alkaline conditions in the cultivated tailings. The pH increase was notably higher in cultivated tailing samples treated with cement+MgO, reaching pH levels ranging from 4.93 to 7.58. Analysis of variance (ANOVA) revealed a highly significant effect of the cement+Mg reagents on the DMY of ryegrass. There was a strong correlation between substrate pH and DMY (r = 0.853, p less than 0.001), with a quadratic regression equation providing the best fit to the data (R? = 0.894, p less than 0.001). In conclusion, the study highlights the potential of an 8% cement combined with 2% MgO for tailing revegetation or cultivation purposes.
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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 | 0.000 |
| 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".