Effect of gold nanoparticles and ciprofloxacin on microbial catabolism: a community-based approach
Bibliographic record
Abstract
The effect of gold nanoparticles (AuNPs) and ciprofloxacin on the catabolism of microbial communities was assessed. This was accomplished through an ex situ methodology designed to give a priori knowledge on the potential for nanoparticles, or other emerging contaminants, to affect the catabolic capabilities of microbial communities in the environment. Microbial communities from a variety of sources were incubated with 31 prespecified carbon sources and either National Institute of Standards and Technology reference material 10-nm AuNPs or ciprofloxacin on 96-well microtiter plates. From the ciprofloxacin study, dose-response curves were generated and exemplified how this method can be used to assess the effect of a toxicant on overall catabolic capabilities of microbial communities. With 10-nm AuNPs at concentrations ranging from 0.01 µg/mL to 0.5 µg/mL, rhizosphere communities from Typha roots were only slightly catabolically inhibited at a single concentration (0.05 µg/mL); no effects were seen on wetland water communities, and a minor positive (i.e., enhanced catabolic capabilities) effect was observed for loamy soil communities. This positive effect might have been because of a thin layer of citrate found on these AuNPs that initiated cometabolism with some of the carbon sources studied. Under the conditions considered, the possible adverse effects of AuNPs on the catabolic capabilities of microbial communities appears to be minimal.
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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 | 0.001 | 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".