A laboratory assay of in situ stabilization of toxic metals in contaminated boreal forest soil using organic and inorganic amendments
Bibliographic record
Abstract
Metal-contaminated soils present a great threat to natural ecosystems and human health. Remediation studies focusing on metal-polluted soils with high organic matter (OM > 20%) are limited. This study evaluated the effectiveness of biochar, compost, diammonium phosphate (DAP), and iron oxides (Fe-O), in immobilizing metals from an OM-rich boreal forest soil contaminated with arsenic (As), cadmium (Cd), copper (Cu), lead (Pb), and zinc (Zn). A laboratory incubation study was conducted with soil amended with biochar (5% w w−1), compost (5% w w−1), DAP (0.2% w w−1), or Fe-O (0.2% w w−1), and a control (without amendment) for 6 months at field capacity moisture content. Metal concentrations were determined in pore water collected at 0, 2, 4, and 6 months after incubation. Soil was extracted sequentially for metals after the incubation period. Metal concentrations in pore water were significantly reduced by different amendments as follows: As by biochar and Fe-O, Cd by biochar, compost, and DAP, Cu by biochar, Pb by compost and DAP, and Zn by biochar and compost. Sequential extractions revealed biochar and (or) compost transferred Cd, Cu, Pb, and Zn from the labile pool to the non-labile pool confirming their effectiveness as amendments for remediation of metal-contaminated OM-rich boreal forest soil.
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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".