Inconsistency in precipitation measurements across the Alaska–Yukon border
Bibliographic record
Abstract
Abstract. This study quantifies the inconsistency in gauge precipitation observations across the border of Alaska and Yukon. It analyses the precipitation measurements by the national standard gauges (National Weather Service (NWS) 8 in. gauge and Nipher gauge) and the bias-corrected data to account for wind effect on the gauge catch, wetting loss and trace events. The bias corrections show a significant amount of errors in the gauge records due to the windy and cold environment in the northern areas of Alaska and Yukon. Monthly corrections increase solid precipitation by 136 % in January and 20 % for July at the Barter Island in Alaska, and about 31 % for January and 4 % for July at the Yukon stations. Regression analyses of the monthly precipitation data show a stronger correlation for the warm months (mainly rainfall) than for cold month (mainly snowfall) between the station pairs, and small changes in the precipitation relationship due to the bias corrections. Double mass curves also indicate changes in the cumulative precipitation over the study periods. This change leads to a smaller and inverted precipitation gradient across the border, representing a significant modification in the precipitation pattern over the northern region. Overall, this study discovers significant inconsistency in the precipitation measurements across the USA–Canada border. This discontinuity is greater for snowfall than for rainfall, as gauge snowfall observations have large errors in windy and cold conditions. This result will certainly impact regional, particularly cross-border, climate and hydrology investigations.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".