Accelerating phytoremediation of degraded agricultural soils utilizing rhizobacteria and endophytes: a review
Bibliographic record
Abstract
Agricultural activities and agro-inputs, particularly chemical fertilizers, farmyard manure, pesticide, sewage sludge, plastic mulch, irrigation, etc., are the primary source of pollutants in farmlands. Agricultural land degradation has become a major concern as it poses a threat to crop productivity. In recent years, microbial-assisted phytoremediation has gained much attention as a promising in situ remediation technology for cleaning polluted soils. Several beneficial rhizobacteria and endophytes facilitate phytoremediation by stimulating innate plant growth-promoting traits such as the production of siderophores, phytohormones, and chelators in addition to their ability to biodegrade contaminants and enhance their removal. Current studies on microbial mediated phytoremediation are demonstrating significant remediation potential. However, there are several challenges in the field that restrict the remediation process. Here we highlight the specific traits, mechanisms, roles, advantages, and problems associated with microbial-assisted phytoremediation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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 teacher head, 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".