Observed trends in precipitation extreme indices as inferred from a homogenized daily precipitation dataset for Canada
Bibliographic record
Abstract
Using the changepoints identified and adjusted to produce the homogenized monthly precipitation dataset, this study developed a homogenized daily precipitation dataset for Canada, in which all data gaps are infilled for the period back to 1900 for southern stations (south of 60˚˚ N), and back to the first day of 1948 or the first day of observation before 1948 for northern stations using advanced spatial interpolation of both monthly and daily values from other stations in the region. The homogenized daily precipitation dataset was then used to assess trends in a set of precipitation extreme indices, including annual maximum one-day (RX1day) and five-day (RX5day) precipitation, as well as annual number of heavy precipitation days, R10mm (annual count of days when precipitation >=10mm). The results show that both annual maximum one-day and five-day precipitation have increased significantly at most stations across Canada over their data record periods, with most stations in the Rocky Mountains and southern Prairies showing insignificance decreases over the period of 1948-2022. Increases in the annual maximum one-day and five-day precipitation are largest and significant in central to northern Canada and in the Maritimes provinces. Annual number of heavy precipitation days has increased significantly at most stations in northern 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".