Influence of Pacific Climate Patterns on Low-Flows in British Columbia and Yukon, Canada
Bibliographic record
Abstract
This study investigated the low-flow response (low-flow frequencies, magnitudes and locations) of rivers in British Columbia and Yukon, Canada to the Pacific Decadal Oscillation (PDO) and the El Niño/Southern Oscillation (ENSO). Using stream discharge data from four hydrometric stations in each of four regions of BC (Southern Coast, Southern Interior, Northern Coast and Northern Interior) and the southern Yukon, coherent responses of low streamflows to PDO and ENSO were identified and examined. Low streamflows were defined as flows less than the 10th percentile on a given day compared to the historical streamflow series for that day. It was found that PDO and ENSO both influenced low streamflows in all study regions except Yukon. The PDO signal influenced low-flows more significantly and consistently than the ENSO signal. However, the PDO signal was modulated by ENSO, either strengthening or weakening low-flows depending on geographic location. ENSO had inconsistent impacts on low-flows with large differences between geographic areas. At the study watersheds in BC, correlation analysis showed that 14 and 13 of the 16 rivers showed significant associations between PDO index and low-flow frequencies and low-flow magnitudes, respectively. Temporal location of low-flow is affected by PDO in coastal BC but not in the interior and Yukon. None of the rivers in Yukon showed significant correlations between low-flows and Pacific climate patterns.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".