CrAssphage as an indicator of groundwater-borne pollution in coastal ecosystems
Bibliographic record
Abstract
Abstract Novel approaches for monitoring coastal water quality changes and identifying associated contaminant source(s) are of growing importance as climate change and population redistribution to coastal zones continue to impact coastal systems. CrAssphage, a virus found in the human gut and shed with fecal matter, is currently gaining popularity as an indicator of human fecal contamination in surface water and groundwater. Here we demonstrate that DNA assays targeting crAssphage genetic fragments can be used to detect pollution from nearshore onsite wastewater treatment systems discharging to the ocean via submarine groundwater discharge. We integrated this novel viral monitoring tool into a field study that characterized the physical hydrogeology (hydraulic gradients, hydraulic conductivity, and seepage fluxes) and surface water and groundwater quality at a study site on the north shore of Nova Scotia, Canada. Increased use of onsite wastewater treatment systems during the summer cottage season coincided with widespread detections of crAssphage in submarine groundwater discharge (4/4 samples) and coastal surface waters (3/8 samples). Conversely, classical fecal pollution indicators based on bacterial targets (Escherichia coli and human-specific Bacteroidales genetic marker (HF183)) were sparsely detected in the samples in the coastal environment (2/12 E. coli samples, 0/12 HF183 samples), likely due to greater attenuation of bacterial contaminants within the subsurface environments. Results from this first application of crAssphage in coastal groundwater contribute to a growing body of research reporting the application of this emerging tracer in various environments impacted by sewage pollution sources.
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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 | 0.002 | 0.001 |
| 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".