Wastewater Effluent Impacts Ammonia-Oxidizing Prokaryotes of the Grand River, Canada
Bibliographic record
Abstract
The Grand River (Ontario, Canada) is impacted by wastewater treatment plants (WWTPs) that release ammonia (NH3 and NH4+) into the river. In-river microbial communities help transform this ammonia into more oxidized compounds (e.g., NO3- or N2), although the spatial distribution and relative abundance of freshwater autotrophic ammonia-oxidizing prokaryotes (AOP) are not well characterized. This study investigated freshwater N cycling within the Grand River, focusing on sediment and water columns, both inside and outside a WWTP effluent plume. The diversity, relative abundance, and nitrification activity of AOP were investigated by denaturing gradient gel electrophoresis (DGGE), quantitative real-time PCR (qPCR), and reverse transcriptase qPCR (RT-qPCR), targeting both 16S rRNA and functional genes, together with activity assays. The analysis of bacterial 16S rRNA gene fingerprints showed that the WWTP effluent strongly affected autochthonous bacterial patterns in the water column but not those associated with sediment nucleic acids. Molecular and activity data demonstrated that ammonia-oxidizing archaea (AOA) were numerically and metabolically dominant in samples taken from outside the WWTP plume, whereas ammonia-oxidizing bacteria (AOB) dominated numerically within the WWTP effluent plume. Potential nitrification rate measurements supported the dominance of AOB activity in downstream sediment. Anaerobic ammonia-oxidizing (anammox) bacteria were detected primarily in sediment nucleic acids. In-river AOA patterns were completely distinct from effluent AOA patterns. This study demonstrates the importance of combined molecular and activity-based studies for disentangling molecular signatures of wastewater effluent from autochthonous prokaryotic communities.
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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 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".