Great Lakes Water Level Trends Using the Moving Statistics Method, with Implications for Climate Change and Cities
Bibliographic record
Abstract
Increasing magnitudes of precipitation and evaporation are predicted for future climate change. Knowing whether these trends are occurring can help water managers plan with respect to future erosion, flooding, and design changes for shoreline infrastructure. Data from all the Laurentian Great Lakes (Erie, Michigan-Huron, Ontario, St. Clair, and Superior) were analyzed here to determine whether these trends are being realized. The Moving-Statistics Method is used here using the moving average and moving standard deviation. It was found that Lakes Erie and St. Clair had the highest moving average trend of 0.5 mm/month, while Lake Ontario had the highest moving standard deviation trend (also 0.2 mm/month). Lake Superior had a decreasing moving average, while Lakes Erie, Michigan-Huron, and St. Clair had decreasing values of moving standard deviation. All lakes had moving average values greater than the measurement margin of error except Lake Superior. It is concluded that Great Lakes water levels have changed in the past and probably continue to change in the future. Property owners land managers can use these results to plan future budgets.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".