Organic amendments potentially stabilize metals in smelter contaminated Arctic soils: An incubation study
Bibliographic record
Abstract
The long-term emission impacts of the nickel processing industry in the Kola Peninsula, the largest source of sulfur dioxide and heavy metals emissions in Northern Europe, have created vast technogenic barrens near the mineral industry complexes. The pace of rehabilitation using the improved remediation technologies to enhance sustainable environmental management and regional economic development is of crucial social and economic importance. In a 120-day incubation experiment, we evaluated the prospects for the restoration of two soils at different degradation stages via carbon pool regulation comparing to mineral ameliorants - NPK fertilizer, and liming agent. Organic additives used included a humic preparation based on an alkaline brown coal extract, wood-derived biochar, and peat-derived gel, supplied by mycorrhizae fungi. The results demonstrate that the selected organic amendments are suitable for restoration of acidic metal contaminated soils. Specifically, the treatments provided a measurable increase in soil carbon content, a marked decrease in acidity, a decrease in extractable metal contents, together with an enhanced nutrient uptake and vegetative growth. A stabilization effect increased from biochar to peat-gel, liming agent and humic preparation, with an accompanying increase in soil pH. Although biochar showed a reduced ability to metal stabilization, the associated treatments were the most productive. The most effective amendments in multi-metallic contaminated soils need to be able to stabilize bioavailability of metals, adjust pH to the optimum for plant growth, and regulate nutrient consumption.
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How this classification was reachedexpand
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 | 0.000 |
| 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) | 0.012 | 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".