An evaluation of the Wyoming Gauge System for snowfall measurement
Bibliographic record
Abstract
The Wyoming snow fence (shield) has been widely used with precipitation gauges for snowfall measurement at more than 25 locations in Alaska since the late 1970s. This gauge's measurements have been taken as the reference for correcting wind‐induced gauge undercatch of snowfall in Alaska. Recently, this fence (shield) was tested in the World Meteorological Organization Solid Precipitation Measurement Intercomparison Project at four locations in the United States of America and Canada for six winter seasons. At the Intercomparison sites an octagonal vertical Double Fence with a Russian Tretyakov gauge or a Universal Belfort recording gauge was installed and used as the Intercomparison Reference (DFIR) to provide true snowfall amounts for this Intercomparison experiment. The Intercomparison data collected were compiled at the four sites that represent a variety of climate, terrain, and exposure. On the basis of these data sets the performance of the Wyoming gauge system for snowfall observations was carefully evaluated against the DFIR and snow cover data. The results show that (1) the mean snow catch efficiency of the Wyoming gauge compared with the DFIR is about 80–90%, (2) there exists a close linear relation between the measurements of the two gauge systems and this relation may serve as a transfer function to adjust the Wyoming gauge records to obtain an estimate of the true snowfall amount, (3) catch efficiency of the Wyoming gauge does not change with wind speed and temperature, and (4) Wyoming gauge measurements are generally compatible to the snowpack water equivalent at selected locations in northern Alaska. These results are important to our effort of determining true snowfall amounts in the high latitudes, and they are also useful for regional hydrologic and climatic analyses.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".