Analysis of the Impacts of Man-Made Features on the Stationarity and Dependence of Monthly Mean Maximum and Minimum Water Levels in the Great Lakes and St. Lawrence River of North America
Bibliographic record
Abstract
Various manmade features (diversions, dredging, regulation, etc.) have affected water levels in the Great Lakes and their outlets since the 19th century. The goal of this study is to analyze the impacts of such features on the stationarity and dependence between monthly mean maximum and minimum water levels in the Great Lakes and St. Lawrence River from 1919 to 2012. As far as stationarity is concerned, the Lombard method brought out shifts in mean and variance values of monthly mean water levels in Lake Ontario and the St. Lawrence River related to regulation of these waterbodies in the wake of the digging of the St. Lawrence Seaway in the mid-1950s. Water level shifts in the other lakes are linked to climate variability. As for the dependence between water levels, the copula method revealed a change in dependence mainly between Lakes Erie and Ontario following regulation of monthly mean maximum and minimum water levels in the latter. The impacts of manmade features primarily affected the temporal variability of monthly mean water levels in Lake Ontario.
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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 | 0.002 |
| 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 | 0.000 |
| 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".