Precipitation gradients across the Continental Divide in the southern Canadian Rockies
Bibliographic record
Abstract
This project examines precipitation patterns across the Continental Divide in the southern Canadian Rockies with a focus on precipitation gradients. This thesis is part of the Storms and Precipitation Across the continental Divide Experiment (SPADE) that occurred between 26 April 2019 and 26 June 2019. Daily meteorological data were also examined between 2011 and 2019. The study area encompassed mountainous topography from the Columbia Valley in eastern British Columbia, to a transect of stations alongside the Foothills in western Alberta, with a range of elevations from about 750 m to about 3500 m above sea level. Local station data were derived from three meteorological stations developed for the SPADE campaign, Nipika Mountain Resort (Nipika), Fortress Junction, Fortress Mountain and a tipping bucket transect. Regional station data were derived from several meteorological station networks with publicly available data. Gridded data included ERA5 and ERA5-Land. Cumulative precipitation amounts were the focus of this study, but temperature, relative humidity, and wind speed and direction were also included in my analysis. The objectives of this project are to examine relationships between precipitation gradients/patterns and elevation, cool and warm seasons, general wind patterns/storm trajectories, and inter-annual and intra-annual variability. Elevation is a predictor of precipitation amounts in our study region, and over most time-frames it was a stronger predictor than latitude and longitude in determining precipitation amounts, but the relationship between elevation and precipitation was not always significant. On average precipitation increases at a rate of 0.39 mm m-1 across the study region when it is examined across an annual period. Warm (summer) and cool (winter) seasons exhibited distinctly different precipitation gradients. A southerly wind component at Nipika was associated with large amounts of precipitation at this site.
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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 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".