Comparison of Winter Precipitation Measurements by Six Tretyakov Gauges at the Valdai Experimental Site
Bibliographic record
Abstract
Analyses of long-term (1991–2010) intercomparison data quantify the consistency of winter precipitation observations by six identical Tretyakov gauges at the Valdai research station in Russia. Relative to the standard Tretyakov gauge, the mean catch ratios are 97 to 106% for dry snow, 94 to 104% for wet snow, 87 to 109% for blowing snow, 96 to 103% for mixed precipitation, and 98 to 101% for winter rain. The differences between the highest and lowest mean catches are about 10 to 11% for snow, 7% for mixed precipitation, and 3% for rain. On average, this difference is about 0.2 mm over the 12-hour observation period. The catch difference for blowing snow is much higher, up to 22%, or an average of 0.6 mm per observation. Comparisons of 12-hour observations show better consistency in gauge performance for low snowfall events and a large variation in gauge catch for high snowfall events. The differences in 12-hour snow catches are mostly less than 2 mm among the six gauges. The differences in the 12-hour observations are less than 1% for rain and 4% for mixed precipitation. Close linear relationships exist between the 12-hour gauge observations for all precipitation types. The maximum differences in gauge snow catches increase very weakly with wind speed, and higher differences are associated with warmer temperatures, from −5°C to 0°C. There is, however, no significant relationship between the maximum catch difference and the mean wind speed or temperature over the 12-hour period.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".