A Real-Time Online Data Product that Automatically Detects Easterly Gap-Flow Events and Precipitation Type in the Columbia River Gorge
Bibliographic record
Abstract
Abstract A real-time, hourly updated, online graphical data product that displays the depth and strength of easterly gap flow in the Columbia River Gorge using a 915-MHz Doppler wind profiler is presented. During precipitation events, this data product also displays observed precipitation accumulation and diagnosed precipitation type using measurements provided by a collocated heated tipping-bucket rain gauge, an optical disdrometer, and temperature and relative humidity sensors. Automated algorithms that determine the existence and depth of the gap flow, as well as precipitation type, are described. The Columbia River Gorge is the only major gap in the Cascade Mountains of Oregon and Washington. Consequently, both easterly and westerly directed gap-flow events are common in this region. Especially during late autumn and winter, easterly gap flow can cause hazardous and damaging weather (e.g., snow, freezing rain, and strong winds) in the Portland, Oregon–Vancouver, Washington metropolitan area. The product described here was developed to help forecasters at the Portland National Weather Service Forecast Office monitor cool-season easterly gap-flow events in order to provide situational awareness and guide warnings to the public about potential weather-related hazards.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.007 | 0.002 |
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".