Evaluation of Native Ohio Plants to Lead and Zinc Contaminated Soils
Bibliographic record
Abstract
Phytoremediation has been acknowledged for quite some time now, as a viable alternative to traditional, more invasive, remediation practices.However, there is a large demand for research relating to the association between specific plants and metal contaminants.The objective to this research is to identify native plants capable of removing or tolerating metal contaminants in soils.Two native Ohio plants commonly found in wet habitat will be evaluated for tolerance and accumulation of zinc and lead.The soil was spiked with two metals, lead and zinc, commonly found along riverbanks in the local area around the Mahoning River.The plants were grown in single metal as well as mixed metal spiked soil for a period of 15 weeks.Once the plants have grown substantially, they were harvested, dried and processed for analysis.The concentrations of metals found in the root area of the soil samples were compared to the spiked soil samples before growth.Both Indian grass and Canada wildrye soil samples showed losses of available metals, with small amounts of metals found in the plant tissue.This indicates that, even though there was limited above ground plant growth, both species may be tolerable to soils containing various concentrations of zinc and lead.
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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".