1 The Spatial and Temporal Variability of Nonfreezing Drizzle in the
Bibliographic record
Abstract
A climatology of nonfreezing drizzle is created using surface observations from 584 stations across the United States and Canada over the 15 years 1976–1990. Drizzle falls 50–200 h a year in most locations in the eastern United States and Canada, whereas drizzle falls less than 50 h a year in the west, except for coastal Alaska and several western basins. The eastern and western halves of North America are separated by a strong gradient in drizzle frequency along roughly 100°W, as large as about an hour a year over 2 km. Forty percent of the stations have a drizzle maximum from November to January, whereas only 13 % of stations have a drizzle maximum from June to August. Drizzle occurrence exhibits a seasonal migration from eastern Canada and the central portion of the Northwest Territories in summer, equatorward to most of the eastern United States and southeast Canada in early winter, to southeastern Texas and the eastern United States in late winter, and back north to eastern Canada in the spring. The diurnal hourly frequency of drizzle across the United States and Canada increases sharply from 0900 UTC to 1200 UTC, followed by a steady decline from 1300 UTC to 2300 UTC. Diurnal drizzle frequency is maximum in the early morning, in agreement with other studies.
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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 | 0.001 | 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) | 0.001 | 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".