Advanced methods for correcting measured precipitation and results of their application in the polar regions of Russia and North America
Bibliographic record
Abstract
The WMO recommendations on solid precipitation correction, based on generalized results of precipitation gauge intercomparisons performed in 1985–1996, do not take into account systematic errors in precipitation measurements such as wind-induced at high winds and false precipitation blown by wind into the precipitation gauge during strong blizzards at low temperatures, typical of high latitudes. To eliminate these biases in solid precipitation measurements in the Arctic latitudes, special procedures are proposed for three different national methods of precipitation measurement in Russia, the United States (Alaska), and Canada. Differences in the correction methods in these countries are caused by differences in the design of instruments, observation technique, climate, and content of data archives for calculating the measurement errors. Results of application of the proposed procedures for precipitation correction in the Arctic regions of the above-mentioned countries are discussed. The results are compared against the maps of corrected precipitation in the world water budget and snow, ice resources atlases and in the Handbook for Climate of the USSR .
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".