The Usefulness of the Lombard Method for Analyzing the Hydrological Impacts of Dams: The Case of the Manouane River Diversion Dam, Quebec, Canada
Bibliographic record
Abstract
The goal of this study is to demonstrate the usefulness of the Lombard method for analyzing dam-induced hydrologic impacts. The method was used to accurately detect the effects of the construction of a diversion dam in 2003 on annual and seasonal maximum and minimum daily flows in the Manouane River, Quebec, Canada, measured from 1980 to 2014. The Lombard method yields results that are nearly identical to results obtained using the monitoring (Kruskal-Wallis test) and long-term trend (Mann-Kendall test) methods. The Lombard method revealed a shift in mean values of annual and seasonal minimum daily flows in 2003, the year the dam was built. This shift is sharp for all four seasons. The dam induced a significant decrease in minimum daily flows in all four seasons. As far as maximum daily flows are concerned, unlike the monitoring method, the Lombard method detected a significant decrease only in the mean values of annual and spring maximum daily flows. This decrease occurred two years prior to the construction of the diversion dam. Instead, this decrease is interpreted to be the result of a significant decrease in spring precipitation after 1997. These hydrological changes are different from those induced by other types of dams in Quebec.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 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".