Great Lakes Water Levels: Curve Fitting, Statistics, and the Unacceptable-Value Statistical Approach
Bibliographic record
Abstract
Water levels in the Great Lakes fluctuate over time. Excessively high levels can cause flooding, erosion, property damage, and loss of human life. Excessively low values inhibit shipping since boats cannot reach the ports and docks due to low draft, thereby causing loss of economic resources. Water level prediction has proven elusive, thereby requiring a new approach to analyzing the historical record of water level values. Methods attempted here include curve fitting, conventional statistics, and a newly introduced Unacceptable-Value Statistical Approach (UVSA). The UVSA comprises only analyzing flooding and drought events. All remaining values are acceptable and, therefore, are not considered. Each flooding/drought event is comprised of all the data above/below the corresponding threshold of acceptable values. Statistics analyzed include the event volume, maximum value, length, and time between flooding or drought events. For curve fitting it was found that none of the over 3000 forms of equations fit the data well. Conventional statistics found that all the lakes had skewed histograms, and increasing levels except Superior, which was flat. The volume of unacceptable events increased for all the lakes except Superior, which was flat. The extreme high and low values increased for some lakes and decreased for others. The event durations also increased for some lakes and decreased for others. The gap time for high events generally decreased and for low events increased for some and decreased for others. This new method (UVSA) shows promise for decision making.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".