Fungal Endophytes for Grass Based Bioremediation: An Endophytic Consortium Isolated from Agrostis stolonifera Stimulates the Growth of Festuca arundinacea in Lead Contaminated Soil
Bibliographic record
Abstract
Bioremediation is an ecologically-friendly approach for the restoration of heavy metal-contaminated sites and can exploit environmental microorganisms such as bacteria and fungi. These microorganisms are capable of removing and/or deactivating pollutants from contaminated substrates through biological and chemical reactions. Moreover, they interact with the natural flora, protecting and stimulating plant growth in these harsh conditions. In this study, we isolated a group of endophytic fungi from Agrostis stolonifera grasses growing on toxic waste from an abandoned lead mine (up to 47,990 Pb mg/kg) and identified them using DNA sequencing (nrITS barcoding). The endophytes were then tested as a consortium of eight strains in a growth chamber experiment in association with the grass Festuca arundinacea at increasing concentrations of lead in the soil to investigate how they influenced several growth parameters. As a general trend, plants treated with endophytes performed better compared to the controls at each concentration of heavy metal, with significant improvements in growth recorded at the highest concentration of lead (800 galena mg/kg). Indeed, this set of plants germinated and tillered significantly earlier compared to the control, with greater production of foliar fresh and dry biomass. Compared with the control, endophyte treated plants germinated more than 1-day earlier and produced 35.91% more plant tillers at 35 days-after-sowing. Our results demonstrate the potential of these fungal endophytes used in a consortium for establishing grassy plant species on lead contaminated soils, which may result in practical applications for heavy metal bioremediation.
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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 | 0.000 | 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".