An Overview of the Second Generation Adjusted Daily Precipitation Dataset for Trend Analysis in Canada
Bibliographic record
Abstract
A second generation adjusted precipitation daily dataset has been prepared for trend analysis in Canada. Daily rainfall and snowfall amounts have been adjusted for 464 stations for known measurement issues such as wind undercatch, evaporation and wetting losses for each type of rain-gauge, snow water equivalent from ruler measurements, trace observations and accumulated amounts from several days. Observations from nearby stations were sometimes combined to create time series that are longer; hence, making them more useful for trend studies. In this new version, daily adjustments are an improvement over the previous version because they are derived from an extended dataset and enhanced metadata knowledge. Datasets were updated to cover recent years, including 2009. The impact of the adjustments on rainfall and snowfall total amounts and trends was examined in detail. As a result of adjustments, total rainfall amounts have increased by 5 to 10% in southern Canada and by more than 20% in the Canadian Arctic, compared to the original observations, while the effect of the adjustments on snowfall were larger and more variable throughout the country. The slope of the rain trend lines decreased as a result of the larger correction applied to the older rain-gauges while the slope of the snow trend lines increased, mainly along the west coast and in the Arctic. Finally, annual and seasonal rainfall and snowfall trends based on the adjusted series were computed for 1950–2009 and 1900–2009. Overall, rainfall has increased across the country while a mix of non-significant increasing and decreasing trends was found during the summer in the Canadian Prairies. Snowfall has increased mainly in the north while a significant decrease was observed in the southwestern part of the country for 1950–2009.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.027 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.007 |
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".