Estimation of phosphorus loads from septic systems to tributaries in the Canadian Lake Erie Basin
Bibliographic record
Abstract
The contribution of septic systems to watershed nutrient loads is poorly quantified although they are often cited as potentially important nutrient sources. The study used a geospatial model to estimate P loads from septic systems to the tributaries of the Canadian Lake Erie Basin to inform Lake Erie nutrient management initiatives. There is currently no inventory of septic systems in the Lake Erie Basin (e.g., numbers and locations of septic systems). Therefore a geospatial model was developed to automatically locate individual septic systems and to use these locations to estimate P load contributions to tributaries. The model was first tested on three subwatersheds in the Canadian Lake Erie Basin before being applied across the Basin. Present-day basin-wide P load estimates reveal that: (i) only a fraction of septic effluent is currently reaching the tributaries due to slow transport and other delays, as well as (ii) P attenuation in the subsurface, range from 23 ± 11 to 68 ± 32 MT/yr. Based on these estimates, septic systems may currently contribute 1.7 ± 0.8–5 ± 2.3% of the P loads to Lake Erie from Canada. However, maximum P load estimates and transient model results show that the contribution of septic systems to P loads will increase over time as slow moving septic-derived groundwater P plumes reach tributaries if aging septic systems are not maintained. This study provides widely applicable new knowledge and methodology; as well as specific findings needed to inform nutrient and septic system management in the Lake Erie Basin.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".