Temporal evolution of low‐flow regimes in Canadian rivers
Bibliographic record
Abstract
This study investigates temporal evolution of 1‐, 7‐, 15‐, and 30‐day annual and seasonal low‐flow regimes of pristine river basins, included in the Canadian reference hydrometric basin network (RHBN), for three time frames: 1974–2003, 1964–2003, and 1954–2003. For the analysis, the RHBN stations are classified into three categories, which correspond to stations where annual low flows occur in winter only, summer only, and both summer and winter seasons, respectively. Unlike in previous studies for the RHBN, such classification is essential to better understand and interpret the identified trends in low‐flow regimes in the RHBN. Nonparametric trend detection and bootstrap resampling approaches are used for the assessment of at‐site temporal trends under the assumption of no persistence or short‐term persistence (STP). The results of the study demonstrate that previously suggested prewhitening and trend‐free prewhitening approaches, for incorporating the effect of STP on trend significance, are not adequate for reliably identifying trends in low‐flow regimes compared to a simple bootstrap‐based approach. The analyses of 10 relatively longer records reveal that trends in low‐flow regimes exhibit fluctuating behavior, and hence, their temporal and spatial interpretations appear to be sensitive to the time frame chosen for the analysis. Furthermore, under the assumption of long‐term persistence (LTP), which is a possible explanation for the fluctuating behavior of trends, many of the significant trends in low‐flow regimes, noted under the assumption of STP, become nonsignificant and their field significance also disappears. Therefore correct identification of STP or LTP in time series of low‐flow regimes is very important as it has serious implications for the detection and interpretation of trends.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 | 0.001 |
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 teacher head, 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".