Evaluation of repeat bathymetric surveys on water surface elevations on the Saint Clair River
Bibliographic record
Abstract
The Great Lakes contain 20 percent of the world’s fresh surface water, a drinking source for 30- million people. The system is vital to manufacturing, largely due to efficient transportation of commodities and an abundance of natural resources. Understanding the water balance and predicting future water levels is important for many industries including commercial navigation, the tourist industry, hydropower and shoreline property owners, just to name a few. These predictions require an understanding of both the hydroclimate drivers across the Great Lakes as well as physical changes in the connecting channels that pass water between each lake. Since the International Upper Great Lakes Study began, the St. Clair River has been the topic of conveyance change investigations. This study tests the sensitivity of water surface elevations to small differences in bathymetric changes. Greater than 80 percent of the surveyed points had less than 0.24 m of change between any combination of survey years. Repeat bathymetric surveys collected in 2007, 2012 and 2021, along with hydrodynamic modeling, find changes in water surface elevations associated with bathymetric change are on the sub-centimeter scale. These changes are put into perspective and compared to known anthropogenic and naturally occurring conveyance changes. While these surveys represent a short duration in the observed water level record of the Great Lakes, they represent an important documentation of the geomorphic state of the St. Clair River. These changes can and should be compared to similar future surveys of the river over longer engineering and geomorphic timescales.
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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 | 0.002 | 0.010 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".