Field accuracy of Canadian rain measurements
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.007 | 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 it