Microbial diversity, tolerance, and biodegradation potential of urban wetlands with different input regimes
Bibliographic record
Abstract
Though microbial transformations are the primary mechanism of contaminant attenuation in wetlands, much remains to be known about microbial communities in urban wetlands. In this study, the microbial communities from urban wetlands with different runoff regimes (i.e., a contaminated remnant wetland, a constructed wetland, and a remnant wetland) were assessed for their capacity to attenuate and tolerate typical urban runoff pollutants. Results from denaturing gradient gel electrophoresis of 16S rRNA genes showed relatively high similarity in community composition among the wetlands. Community-level physiological profiles had similar results but exhibited within-site variation in both the contaminated remnant and remnant wetlands. All wetland communities were less tolerant to copper than 2,4-dichlorophenoxyacetic acid; however, the contaminated remnant wetland had the highest tolerance. All study wetlands had a limited capacity to biodegrade model chlorinated aromatic compounds (e.g., 2,4-dichlorophenoxyacetic acid and 3-chlorobenzoate). Though having different input regimes and contaminant exposure histories, the study wetlands were generally similar with respect to microbial community diversity and function. Additionally, the generally low capacity for these wetlands to biodegrade mobile chlorinated organic contaminants offers preliminary insight into the limited ecosystem services these wetlands may provide in urban environments.
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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".