PHYTOSTABLIZATION OF SULPHIDE MINE TAILINGS
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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 it