Microbial bioremediation of persistent organic pollutants in plant tissues provides crop growth promoting liquid fertilizer
Bibliographic record
Abstract
Constructed wetlands are used to clean domestic wastewater via phytoremediation, commonly involving the use of reeds. The process results in the production of large amounts of polluted plant tissues, which are then considered unusable waste products. In this study, the reusability of reeds and nettle-polluted tissues is investigated. Fermenting contaminated plant tissues to produce liquid fertilizer is a sustainable means to remove 87-95% of persistent organic pollutants. A multiomics approach combining metabolomics and amplicon metagenomics is used to analyze the mechanisms that occur during fertilizer production from polluted plant tissues and identify the microbes that are likely key for this transformation. A consortium of bacteria and fungi with cellulolytic activity is identified. In addition, the obtained liquid fertilizer positively impacts plant growth in the presence of pathogens and therefore exhibits potential application in farming. This approach may be a simple, commercially attractive solution for the management of contaminated plant tissues originating from constructed wetlands, which are currently considered problematic, useless waste products.
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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".