Bibliographic record
Abstract
A study of trends and variability of low flow characteristics was conducted for the Reference Hydrometric Basin Network (RHBN). A seven-day low-flow index from 57 hydrometric stations was extracted and examined to detect trends and changes in the timing of summer and winter seven-day low-flows. A modified Mann-Kendall (MK) nonparametric trend test was applied to the time series at a 0.05 significance level. The variance of the S statistic was modified if the absolute value of serial correlation was significant at a 0.1 significance level. Numerical analysis indicated that northern Canada (stations located above latitude 60oN) experienced an increasing significant trend in seven-day low-flows. A significant downward trend dominated the Atlantic Provinces and southern British Columbia. No evidence of significant trends in the Prairies and eastern Ontario was found. Summer seven-day low-flow shifted to arrive earlier in the year in the Atlantic Provinces and southern Ontario; however, it arrived later in the year in western and northwestern Canada. In 88% of significant trends, winter seven-day low-flow shifted to arrive earlier. Although both winter and summer low flows experienced a shift towards earlier dates in the eastern part of the country, they were in opposite direction in western Canada where winter seven-day low-flows were arriving earlier whereas summer seven-day low-flows were arriving later in the year.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".