Nanosilver inhibits nitrification and reduces ammonia‐oxidising bacterial but not archaeal <i>amoA</i> gene abundance in estuarine sediments
Bibliographic record
Abstract
Summary Silver nanoparticles (AgNPs) enter estuaries via wastewater treatment effluents, where they can inhibit microorganisms, because of their antimicrobial properties. Ammonia‐oxidising bacteria (AOB) and archaea (AOA) are involved in the first step of nitrification and are important to ecosystem function, especially where effluent discharge results in high nitrogen inputs. Here, we investigated the effect of a pulse addition of AgNPs on AOB and AOA ammonia monooxygenase ( amoA ) gene abundances and benthic nitrification potential rates (NPR) in low‐salinity and mesohaline estuarine sediments. Whilst exposure to 0.5 mg L −1 AgNPs had no significant effect on amoA gene abundances or NPR, 50 mg L −1 AgNPs significantly decreased AOB amoA gene abundance (up to 76% over 14 days), and significantly decreased NPR by 20‐fold in low‐salinity sediments and by twofold in mesohaline sediments, after one day. AgNP behaviour differed between sites, whereby greater aggregation occurred in mesohaline waters (possibly due to higher salinity), which may have reduced toxicity. In conclusion, AgNPs have the potential to reduce ammonia oxidation in estuarine sediments, particularly where AgNPs accumulate over time and reach high concentrations. This could lead to long‐term risks to nitrification, especially in polyhaline estuaries where ammonia‐oxidation is largely driven by AOB.
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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) | 0.001 | 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".