Heavy Metal Sorption and Hydraulic Conductivity Studies Using Three Types of Bentonite Admixes
Bibliographic record
Abstract
Bentonite, forest soil, and spruce bark were submitted to batch adsorption testing, leaching cell testing, and selective sequential extractions (SSEs) to investigate the heavy-metal compatibility of clay barriers and the potential of forest soil and spruce bark as clay barrier materials. The materials ranked as follows according to sorption capacity: forest soil > bentonite = spruce bark. The hydraulic conductivity values of heavy-metal leachates were two orders of magnitude greater than those of the blank (0.01 mol calcium nitrate) leachate. The forest soil admix ranked first in terms of heavy-metal retention capacity and breakthrough points. The mobility of Cd was 4.5 times higher than that of Pb, and Cu was 2.5 times more mobile than Pb. The leaching cell and SSE results indicate that heavy metals cause significant preferential channeling. The SSE results show that the addition of forest soil and spruce bark to clay barrier mixes promotes heavy-metal fixation.
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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) | 0.001 | 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) | 0.000 | 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".