The Effect of pH on Metal Accumulation in Two <i>Alyssum</i> Species
Bibliographic record
Abstract
Nickel phytoextraction using hyperaccumulator plants offers a potential for profit while decontaminating soils. Although soil pH is considered a key factor in metal uptake by crops, little is known about soil pH effects on metal uptake by hyperaccumulator plants. Two Ni and Co hyperaccumulators, Alyssum murale and A. corsicum, were grown in Quarry muck (Terric Haplohemist) and Welland (Typic Epiaquoll) soils contaminated by a Ni refinery in Port Colborne, Ontario, Canada, and in the serpentine Brockman soil (Typic Xerochrepts) from Oregon, USA. Soils were acidified and limed to cover pH from strongly acidic to mildly alkaline. Alyssum grown in both industrially contaminated soils exhibited increased Ni concentration in shoots as soil pH increased despite a decrease in water-soluble soil Ni, opposite to that seen with agricultural crop plants. A small decrease in Alyssum shoot Ni concentration as soil pH increased was observed in the serpentine soil. The highest fraction of total soil Ni was phytoextracted from Quarry muck (6.3%), followed by Welland (4.7%), and Brockman (0.84%). Maximum Ni phytoextraction was achieved at pH 7.3, 7.7, and 6.4 in the Quarry, Welland, and Brockman soils, respectively. Cobalt concentrations in shoots increased with soil pH increase in the Quarry muck, but decreased in the Welland soil. Plants extracted 1.71, 0.83, and 0.05% of the total soil Co from Welland, Quarry, and Brockman, respectively. The differences in uptake pattern of Ni and Co by Alyssum from different soils and pH were probably related to the differences in organic matter and iron contents of the soils.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".