Bibliographic record
Abstract
ABSTRACT The detrended fluctuation analysis (DFA) and multifractal DFA, which can detect nonstationarities of time series with trends, were applied to study long‐term persistence (LTP) and multifractal behaviour of 100 stations of daily precipitation and 145 stations of streamflow time series of Canada. Results show that all precipitation time series showed LTP at both small and large time scales, while streamflow time series generally showed nonstationary behaviour at small time scales and LTP at large time scales. The significant multifractal behaviour of Canadian precipitation and streamflow data can be accurately described by the universal multifractal model and the modified multiplicative cascade model. Precipitation over central Canada showed stronger multifractality than that of western and eastern Canada, while multifractality of streamflow data is less spatially homogeneous. The multifractal strength of precipitation is generally smaller than that of streamflow. Eleven (9) out of 100 precipitation stations showed positive (negative) temporal trends in parameters derived using the universal multifractal model, and about half of the stations whose streamflow data exhibited statistically significant abrupt change points showed a weakening or strengthening in the multifractal strength moving from the pre‐change to the post‐change periods. Differences in the multifractal strength between Canadian precipitation and streamflow data suggest that the persistence of streamflow was not only because streamflow is more autocorrelated than precipitation but also it is more consistently affected by human activities such as streamflow regulation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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".