Bibliographic record
Abstract
Daily historical rain‐gauge data from several Canadian sources and field experiments were compared to the World Meteorological Organization (WMO) pit gauge rainfall measurements in order to determine the accuracies for different operational rain gauges. The detailed technical description of the main Canadian precipitation gauges assisted in understanding the associated accuracies and the need for adjustments for rain‐gauge errors. All gauges, including the pit gauge, reported less than the actual rainfall. The corrections for wind, funnel wetting, evaporation and receiver retention improved the overall accuracy of the manual gauges. The range of rainfall measurements from different manual gauges was greatly reduced after applying the correction factors which were determined through a series of precision measurements. The recently introduced Hydrological Services TB3 tipping bucket rain gauge and the Geonor T‐200B precipitation gauge improved rainfall catch efficiencies compared to the older Meteorological Service of Canada (MSC) tipping bucket and F&P/Belfort gauges with error values of ‐3.5% for the TB3 and ‐4.7% for the Geonor. The manual Type B gauge, in service for more than thirty years, was found to be the best rain gauge and provided the most accurate values based on all the reported rainfall field experiments with an average bias of only ‐0.6% compared to the raw pit gauge data.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".