Temporal Variability of Monthly Daily Extreme Water Levels in the St. Lawrence River at the Sorel Station from 1912 to 2010
Bibliographic record
Abstract
Although climate models predict that the impacts of climate change on the temporal variability of water levels in the St. Lawrence River will be seasonally-dependent, such a seasonal effect on the current variability of extreme water levels has never been analyzed. To address this, we analyzed the temporal variability of three hydrological variables (monthly daily maximums and minimums, as well as their ratio) of water levels in the St. Lawrence River measured at the Sorel station since 1912, as they relate to climate indices. As for stationarity, the shifts in the mean values of maximum and minimum water levels revealed by the Lombard method took place prior to 1970 for spring water levels, but after that year, for winter water levels. Changes in the winter stationarity are thought to mainly relate to the decreasing snowfall observed in the St. Lawrence River watershed after 1970. In contrast, for spring, these changes are likely primarily related to human activity (digging of the St. Lawrence Seaway and construction of dams). Two shifts in the mean values of fall minimum extreme water levels were highlighted. The first of these shifts, which occurred in the first half of the 1960s decade, can also be linked to human activity (digging of the St. Lawrence Seaway and construction of dams), whereas the second shift, observed after the 1970s for the months of November and December, can be linked to decreasing amounts of snow in winter. AMO (Atlantic Multidecadal Oscillation) is the climate index that is most frequently correlated negatively with the hydrologic variables, mainly in winter and spring.
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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".