Correcting wind‐induced bias in solid precipitation measurements in case of limited and uncertain data
Bibliographic record
Abstract
Abstract Automatic precipitation gauges tend to underestimate solid precipitation in the presence of wind. Loss as a function of wind speed is typically evaluated by comparing the gauge with a more accurate measurement made using a double‐fence intercomparison reference gauge (DFIR). For small precipitation events, small errors in the observations can induce large errors in the ‘catch’ ratio, i.e. the ratio of the automatic gauge measurement to the DFIR observation. For this reason, precipitation events of less than 3 mm are typically discarded before performing the regression analysis. This can mean discarding more than 90% of the observations. This paper shows how the method of weighted least squares can be used to perform a regression analysis that can take into account the whole sample to provide a more accurate estimation of the relationship between the catch ratio and the wind speed. This methodology is then used to obtain an adjustment curve for a shielded Geonor T‐200B precipitation gauge in Northern Québec. Copyright © 2008 John Wiley & Sons, Ltd and Her Majesty the Queen in right of Canada.
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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 | 0.003 | 0.015 |
| 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 | 0.001 |
| Open science | 0.001 | 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".