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.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".