Lake Michigan-Huron Water Level Decline due to Hydraulic Scour of the St. Clair River
Bibliographic record
Abstract
Upper Great Lakes water levels are currently experiencing a precipitous decline. The December monthly mean water levels for Lakes Superior, Michigan-Huron, and St. Clair were 17, 15, and 2 inches, respectively, below average when compared to long-term (1918–2005) averages. At the same time, Lake Erie and Lake Ontario were 7 and 12 inches above average. Concern about declining Lake Michigan-Huron water level from public and private organizations has lead to increased study of the fluvial geomorphology of the St. Clair River and its contribution to the steep decline of the Lake Michigan-Huron water level. The position of this paper, based upon historical construction and dredging records, is that the increased hydraulic scour rate of the St. Clair River is an anthropogenic effect of riverbed armor layer removal influenced by navigational and commercial dredging projects. Hydraulic scour increases the outflow capacity of the St. Clair River which results in a water level decline on Lake Michigan-Huron. The goal of this technical paper is to summarize the causal relationship between St. Clair River erosion and decreasing Lake Michigan-Huron water level. An additional concluding section has been added to suggest an economical mitigation measure.
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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".