Causes of variability in monthly Great Lakes water supplies and lake levels
Bibliographic record
Abstract
The purpose of this study was to identify those water budget components of the Great Lakes that have most frequently been a major cause of anomalous net basin supplies (NBS) and of rising and falling lake levels at the monthly time scale. Principal component analysis and a simple counting of relative frequencies revealed that on the upper lakes NBS anomalies are most sensitive to overlake precipitation, but on the lower lakes they are most sensitive to runoff. This shift is due to a downstream increase in the magnitude and variability of runoff. Evaporation variability plays a larger role in the NBS of the upper than the lower lakes and is most important during dry months. During wet months evaporation is not as much suppressed as one might assume from the simple cloud cover/insolation/temperature/evaporation relationship; this is most likely due to an increase in wind speed. High and rising as well as low and falling lake levels are the result of anomalous NBS on all lakes and represent condition beyond the capabilities of lake-level regulations. Changing conditions -low but rising levels or high but falling levels -are the result of anomalous NBS for all of the lakes except Ontario, for which almost all such changes are achieved by regulating the outflow.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".