The Red Mud Accident in Ajka (Hungary): Plant Toxicity and Trace Metal Bioavailability in Red Mud Contaminated Soil
Bibliographic record
Abstract
The red mud accident of October 4, 2010, in Ajka (Hungary) contaminated a vast area with caustic, saline red mud (pH 12) that contains several toxic trace metals above soil limits. Red mud was characterized and its toxicity for plants was measured to evaluate the soil contamination risks. Red mud radioactivity (e.g., (238)U) is about 10-fold above soil background and previous assessments revealed that radiation risk is limited to indoor radon. The plant toxicity and trace metal availability was tested with mixtures of this red mud and a local noncontaminated soil up to a 16% dry weight fraction. Increasing red mud applications increased soil pH to maximally 8.3 and soil solution EC to 12 dS m(-1). Shoot yield of barley seedlings was affected by 25% at 5% red mud in soil and above. Red mud increased shoot Cu, Cr, Fe, and Ni concentrations; however, none of these exceed toxic limits reported elsewhere. Moreover, NaOH amended reference treatments showed similar yield reductions and similar changes in shoot composition. Foliar diagnostics suggest that Na (>1% in affected plants) is the prime cause of growth effects in red mud and in corresponding NaOH amended soils. Shoot Cd and Pb concentrations decreased by increasing applications or were unaffected. Leaching amended soils (3 pore volumes) did not completely remove the Na injury, likely because soil structure was deteriorated. The foliar composition and the NaOH reference experiment allow concluding that the Na salinity, not the trace metal contamination, is the main concern for this red mud in 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) | 0.000 | 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) | 0.000 | 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".