Daily Detroit river total phosphorus loads to Lake Erie from water treatment plant turbidity
Bibliographic record
Abstract
The Great Lakes Water Quality Agreement established the western and central basin of Lake Erie total phosphorus (TP) target of 6000 metric tons per year. Models that develop load-response curves showed that daily loads and annual loads of the Detroit River are important. Direct measurements near the river mouth are difficult due to Lake Erie seiches and surface oscillations. Therefore, an alternative approach for estimating daily loads is needed. We show that existing turbidity-TP relationships can be applied to water treatment plant (WTP) turbidity to develop daily load estimates by those responsible for routine monitoring. We show how turbidity measured at WTP are comparable to those measured in the river. WTP uses an existing infrastructure that is of high temporal resolution, so agencies charged with determining loads may use this network. By using daily water flux and the WTP-based observations, daily TP concentration estimates and loads are applied upstream near Belle Isle and downstream near Fighting Island. By adding the respective loads to the river, we obtain daily TP flux estimates to Lake Erie. Due to well known turbidity gradients, the Windsor WTP needs an adjustment to better reflect turbidity in the entire river. After adjusting, the summed daily rates to Lake Erie from both stations are comparable each other and to other rates. We also show the 2019–2024 annual averages are approximately 15 % greater than that estimated by the Environmental Protection Agency (EPA) and Environment and Climate Change Canada (ECCC). These daily and annual load estimates can be useful augmentations of more traditional monitoring efforts that provide only annual loads.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".