On the Role of a Large Shallow Lake (Lake St. Clair, USA‐Canada) in Modulating Phosphorus Loads to Lake Erie
Bibliographic record
Abstract
Abstract It is often assumed that large shallow water bodies are net sediment nondepositional annually and that if they have nutrient loads from multiple sources, those loads are quickly homogenized before exiting the water bodies. Where this is not the case, it impacts understanding and predicting consequences of nutrient load reductions, both for the water body and for those downstream of it. We applied a three‐dimensional ecological model to a large shallow lake, Lake St. Clair (US/Canada), to quantify the total and dissolved reactive phosphorus (TP and DRP) transport and retention, and construct tributary‐specific relationships between phosphorus load to the lake and the amount of phosphorus that leaves the lake for the three major tributaries. Lake St. Clair is situated between the St. Clair and Detroit rivers, the latter enters Lake Erie. Efforts to reduce Lake Erie's re‐eutrophication requires an understanding of nutrient transport and retention in each of its subwatersheds including those that feed indirectly via Lake St. Clair. We found that over the simulation period, the lake retained a significant portion of TP (17%) and DRP (35%) load and that TP and DRP retention was spatially variable and largely controlled by a combination of lake depth, resuspension, and plankton uptake. Compared to the Clinton and Sydenham rivers, the Thames River contributed a larger proportion of its load to the lake's outflow. However, because the lake's load is dominated by the St. Clair River, 40% reductions of nutrients from those subwatersheds will result in less than a 5% reduction in the load to Lake Erie.
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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 | 0.000 | 0.000 |
| 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".