Remediating Ni-phytotoxicity of contaminated Quarry muck soil using limestone and hydrous iron oxide
Bibliographic record
Abstract
Remediation of excessive soil metals in situ is receiving new attention because the alternative, soil removal and replacement, is very expensive, requires disposal of the removed soil and may achieve no better environmental remediation than the in situ treatments. A factorial pot experiment was conducted with two muck soils contaminated by a Ni refinery; we tested the effectiveness of making the soil calcareous and addition of freshly precipitated hydous ferric oxide (HFO) in reducing soil Ni phytotoxicity to the Ni-sensitive crops, oat and redbeet, and a Ni-resistant crop, wheat. Fertilized but otherwise untreated soil caused significant Ni phytotoxicity to oats and redbeet, but not to wheat, on both soils. Adding limestone reduced the concentration of Ni in shoots of all species and alleviated the symptoms specific to Ni phytotoxicity in oat (banded chlorosis). The addition of HFO was more effective in reducing shoot Ni concentration in the redbeets than in crops from the Poaceae family. Both amendments induced phosphorus and/or manganese deficiency depending on the crop tested. The experiment indicates that some combination of limestone and Fe oxides can readily remediate Ni phytotoxicity of the tested soils, but that Mn and P fertilizers would be needed to achieve practical in situ remediation of Ni phytotoxicity of Quarry muck (Terric Mesisol). Key words: Nickel, soil, plant, phytotoxicity, remediation
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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) | 0.000 | 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".