Microbe–sediment interactions in Great Lakes recreational waters: Implications for human health risk
Bibliographic record
Abstract
Microbial assessments of recreational water have traditionally focused on culturing or DNA-based approaches of the planktonic water column, omitting influence from microbe-sediment relationships. Sediment (bed and suspended) has been shown to often harbour levels of bacteria higher than the planktonic phase. The fate of suspended sediment (SS) bacteria is extensively related to transport dynamics (e.g., deposition) of the associated sediment/floc. When hydraulic energy allows, SS will settle, introducing new (potentially pathogenic) organisms to the bed. With turbulence, including waves, currents and swimmers, the risk of human ingestion is elevated due to resuspension of bed sediment and associated microbes. This research used multiplex nanofluidic reverse transcriptase quantitative PCR on RNA of bacteria associated with bed and SS to explore the active bacteria in freshwater shorelines. Bacterial genes of human health concern regarding recreational water use were targeted, such as faecal indicator bacteria (FIB), microbial source tracking genes and virulence factors from waterborne pathogens. Results indicate avian sources (i.e., gulls, geese) to be the largest nonpoint source of FIB associated with sediment in Great Lakes shorelines. This research introduces a novel approach to microbial water quality assessments and enhances our understanding of microbe-sediment dynamics and the quality of freshwater beaches.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".