Long-Term Dependence of Annual Peak Flows of Canadian Rivers: Two Decades Later
Bibliographic record
Abstract
Both short- and long-term dependence of the peak flow series of 90 Canadian rivers were analyzed two decades ago. It showed that although short-term dependence was practically absent for most of the flow series, significant long-term dependence was present for a large number of rivers tested. With 20 or more years of additional data available today, the authors analyzed 57 rivers (only 57/90 were suitable for analysis) for: a) short-term dependence using several parametric and non-parametric tests; b) long-term dependence using a resampling-based Hurst’s K; and c) trend using Mann-Kendall’s test. Results showed that as expected, short-term dependence is practically absent in all rivers before or after the additional data. However, the percentage of rivers showing long-term dependence remains high. The trend test showed that most of the rivers showed no trends before or after the additional records were added. However, several rivers showed a downward trend, and a few showed an upward trend before and after the additional records were added. This study showed that sample statistics and the associated statistical significance tests can change unpredictably over time. Hence engineering decisions made in the past need to be re-visited and cannot assumed to remain unchanged especially when dealing with natural phenomenon such as annual peak flows.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".