Spatiotemporal distribution and trend analyses of atmospheric rivers affecting British Columbia's Nechako Watershed
Bibliographic record
Abstract
Abstract Research and shared interest in atmospheric rivers (ARs) have increased significantly in recent years, alongside technological improvements that allow better comprehension of these storms. The Nechako River Basin (NRB) in British Columbia, Canada, is significantly affected by ARs originating in the Pacific Ocean. This work analyses the frequency, intensity, duration and trends of ARs in two regions (South and North) near the NRB. Analyses are based on data provided by an updated AR catalogue. The AR catalogue is matched on a daily scale to an adaptation of the AR scale to compile so‐called AR‐Days (ARDs). In the South region, ARDs exhibit stronger associations with hydroclimatic variables total precipitation, rain, snow and snow depth water equivalent (SWE). The Mann–Kendall (MK) trend test was applied to 364 time series created by combining the two closest AR‐monitored regions to the NRB with the annual and seasonal scales of climate data and the adapted AR scale (ARD0‐ARD5). Results show higher AR frequency of mainly beneficial ARDs during fall and a significant reduction of ARD1‐ARD3 in both analysed regions. Rain and total precipitation related to ARD2‐ARD3 also present significant decreasing trends for most sub‐basins of the NRB. The MK test shows a shift in water contribution from total precipitation and rainfall linked to more potentially dangerous ARDs to short‐duration, beneficial ARDs (ARD0). Rain from non‐AR‐related meteorological systems presents an increasing trend for the Upper Nechako sub‐basin, where the Nechako Reservoir is located. Trends are mainly for AR‐related total precipitation and rainfall, and in the northern part of the NRB, results point to the increase of AR‐related SWE.
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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 | 0.001 |
| 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 | 0.000 | 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".