Periodic structures of Great Lakes levels using wavelet analysis
Bibliographic record
Abstract
Periodic structures of Great Lakes levels using wavelet analysis The recently advanced approach of wavelet transforms is applied to the analysis of lake levels. The aim of this study is to investigate the variability of lake levels in four lakes in the Great Lakes region where the method of continuous wavelet transform and global spectra are used. The analysis of lake-level variations in the time-scale domain incorporates the method of continuous wavelet transform and the global spectrum. Four lake levels, Lake Erie, Lake Michigan, Lake Ontario, and Lake Superior in the Great Lakes region were selected for the analysis. Monthly lake level records at selected locations were analyzed by wavelet transform for the period 1919 to 2004. The periodic structures of the Great Lakes levels revealed a spectrum between the 1-year and 43- year scale level. It is found that major lake levels periodicities are generally the annual cycle. Lake Michigan levels show different periodicities from Lake Erie and Lake Superior and Lake Ontario levels. Lake Michigan showed generally long-term (more than 10 years) periodicities. It was shown that the Michigan Lake shows much stronger influences of inter-annual atmospheric variability than the other three lakes. The other result was that some interesting correlations between global spectrums of the lake levels from the same climatic region were found.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".