Bibliographic record
Abstract
Mine tailings are one of the main sources of dissolved arsenic (As) in groundwater. In the present study, an investigation was conducted on the efficiency of sophorolipids, at different concentrations and pH levels and at two different temperatures (15°C and 23°C), to remove arsenic and heavy metals from mine tailings. Furthermore, the effect of sophorolipids on the speciation of arsenic and the effectiveness of sophorolipids on different fractions of the specimens were investigated by way of sequential extraction. After the treatment of the specimens with a solution of 1% sophorolipids at pH 5 and 23°C, 0·7% of the total removed arsenic was from the water-soluble portion of the mine tailing sample, 0·7% was from the exchangeable portion, 0·6% was from the carbonates, 29·9% was from the oxide/hydroxide fraction, 3·0% was from the organic portion and 65·1% was from the residual fraction of the specimen. The results from this study can help develop a sustainable and environment-friendly solution for the remediation of mine tailings.
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 | 0.000 | 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".