Broad-Scale Analysis of Factors Influencing Inputs of Domestic Wastewater Constituents from Onsite Wastewater Treatment Systems to Streams
Bibliographic record
Abstract
This study provides new field-based evidence of the physical and socioeconomic watershed factors and streamflow conditions that influence effluent inputs to streams from onsite wastewater treatment systems (OWTSs), including potential differences between inputs via slow (groundwater) and more rapid (subsurface preferential, overland, direct pipe) transport pathways. Stream sampling data were compiled for 46 watersheds in Ontario, Canada, with analyses including a conservative chemical tracer (acesulfame) representing all (slow and rapid) pathways and a nonconservative human fecal bacteria tracer (HF183) representing only rapid pathways. Acesulfame stream concentrations ranged from tens to over 1000 ng/L, indicating OWTS effluent inputs to streams are widespread. Additionally, HF183 was detected in >20% of stream samples, indicating the prevalence of rapid pathways. Linear mixed-effects models indicate that the percentage of OWTS effluent reaching streams, based on acesulfame data, was higher under high flow conditions and in watersheds with older houses, more houses within 200 m of a stream, and a lower topographic wetness index. Higher human fecal contamination, based on HF183 detections, was observed in streams that drain watersheds with high OWTS density, more houses within 200 m of a stream, and a higher topographic wetness index. These findings support improved pollutant load predictions and better targeting of watersheds for OWTS management.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".