Arbuscular Mycorrhizal Fungi in the Phytostabilization of Soil Degraded by Manganese Mining
Bibliographic record
Abstract
Mining and processing of manganese (Mn) minerals are activities that may result in the generation of large amounts of wastes and serious environmental impacts. Several strategies have been employed to remediate areas with high Mn concentrations, but many of them imply high investments and high risk of secondary pollution. This study aimed to evaluate the phytostabilization potential of Mimosa caesalpiniaefolia Benth. in Mn mining soil influenced by inoculation with arbuscular mycorrhizal fungi (AMF). The experimental design was completely randomized, with four treatments [not inoculated (control), inoculated with Rhizophagus clarus; inoculated with Claroideoglomus etunicatum and inoculated with Rhizophagus clarus + Claroideoglomus etunicatum (Mix)], and four replicates. Inoculation with Mix and C. etunicatum had higher efficiency in protecting plants against excess Mn, due to the greater retention of this element in the roots and lower translocation to the shoots. Inoculation with R. clarus did not influence plant development and reduction of Mn contents in the shoots. The association of the AMF Mix and C. etunicatum with the species Mimosa caesalpiniaefolia Benth. enhances Mn phytostabilization in mining soils with high concentration of this element. The use of multivariate analyses proved to be an important tool with respect to the behavior of biometric, chemical and microbiological variables in mining soil with high Mn concentration.
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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) | 0.000 | 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".