A Comparison of Trends in Hydrological Variables for Two Watersheds in the Mackenzie River Basin
Bibliographic record
Abstract
A study of trends and variability of hydrological variables was conducted for natural streamflow gauging stations within two watersheds that are important sources of flow within the Mackenzie River Basin. A comparison was made between trend results for the Liard River Basin and for the Athabasca River Basin. These basins represent a north-south transect of high elevation headwater basins within the Mackenzie River system and are significant since they produce 34% of the annual flow, while occupying only 24% of the total drainage area. Trend analysis was conducted using the Mann-Kendall test with an approach that corrects for serial correlation. The global (or field) significance of the results for each watershed was evaluated using a bootstrap resampling approach. The relationships between trends in hydrological variables and trends in meteorological variables were investigated using partial correlation analysis. The results reveal more trends in some hydrological variables than are expected to occur by chance. In general, both basins exhibit an increase in winter flows and some increase in spring runoff. These increased flows are somewhat offset by decreases (not field significant) in summertime flow. Almost 50% of the stations used in the analysis show an increasing trend in annual minimum flows. Other differences in trend responses are noted for the two watersheds and possible explanations for the differences are hypothesized.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".