In Situ Remediation of Nickel Phytotoxicity for Different Plant Species
Bibliographic record
Abstract
Acidic nickel (Ni)-contaminated soils in the vicinity of a Ni refinery at Port Colborne (Ontario, Canada) cause Ni phytotoxicity and require remediation. Thus, a greenhouse test with 11 plant species with a wide range of susceptibility to Ni toxicity was conducted to determine if Ni phytotoxicity of all species could be ameliorated by a high rate of limestone. At the original pH of 5.2, the Welland soil (Typic Epiaquoll; 2900 mg kg−1 Ni) was severely phytotoxic to all plant species tested. Toxicity symptoms in dicots included interveinal chlorosis and necrosis of leaves. In grasses, a banded chlorosis was present. Two limestone rates, 2.5 and 50 Mg ha−1, were included in the test. Both liming and plant species significantly affected soil pH, and 0.01 M Sr(NO3)2-extractable soil Ni. Increase in pH exponentially decreased Sr(NO3)2-extractable soil Ni. Grass species were more resistant to Ni toxicity than dicots. Liming soil to pH of 5.9–6.3 enabled good growth of several grass species, but dicot species were still stunted or died. Making the soil calcareous (pH 7.7–7.8) ameliorated Ni toxicity of this highly contaminated soil for all species tested. Concentration of Ni in shoots associated with 25% yield reduction varied among species ranging from 9 to 122 mg kg−1 dry shoots.
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 | 0.000 | 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".