Fungal Inoculation and Elevated CO<sub>2</sub>Mediate Growth of<i>Lolium Mutiforum</i>and<i>Phytolacca Americana</i>, Metal Uptake, and Metal Bioavailability in Metal-Contaminated Soil: Evidence from DGT Measurement
Bibliographic record
Abstract
Fungal inoculation and elevated CO2 may mediate plant growth and uptake of heavy metals, but little evidence from Diffusive Gradients in Thin-films (DGT) measurement has been obtained to characterize the process. Lolium mutiforum and Phytolacca americana were grown at ambient and elevated CO2 on naturally Cd and Pb contaminated soils inoculated with and without Trichoderma asperellum strain C3 or Penicillium chrysogenum strain D4, to investigate plant growth, metal uptake, and metal bioavailability responses. Fungal inoculation increased plant biomass and shoot/root Cd and Pb concentrations. Elevated CO2 significantly increased plants biomass, but decreased Cd and Pb concentrations in shoot/root to various extents, leading to a metal dilution phenomenon. Total Cd and Pb uptake by plants, and DGT-measured Cd and Pb concentrations in rhizosphere soils, were higher in all fungal inoculation and elevated CO2 treatments than control treatments, with the combined treatments having more influence than either treatment alone. Metal dilution phenomenon occurred because the increase in DGT-measured bioavailable metal pools in plant rhizosphere due to elevated CO2 was unable to match the increase in requirement for plant uptake of metals due to plant biomass increase.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 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".