Projection of future daily precipitation series and extreme events by using a multi-site statistical downscaling model over the great Montréal area, Québec, Canada
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
This study suggested strategies to project future precipitation series based on a multi-site hybrid SDM (statistical downscaling model), which can downscale precipitation series at multiple observation sites simultaneously by combining the multivariate multiple linear regression (MMLR) model and the stochastic randomization procedure. The hybrid SDM and future projection methodologies applied to 10 observation sites located in the great area of Montréal, Québec, Canada. Six future independent precipitation series were projected from six sets of future atmospheric predictors using three AOGCMs (Atmosphere-Ocean Global Climate Models, i.e. CGCM2, CGCM3, HadCM3) and three IPCC SRES emission scenarios (B2, A1B and A2). Downscaled climate change signals on wet/dry sequences and extreme indices of precipitation time series were evaluated over the future period from 2060 to 2099 with respect to the historical period from 1961 to 2000. The future scenarios of all three AOGCMs showed a consistent increase of 7.9–44.6% in winter while only those of HadCM3 and CGCM3 showed a decrease of 2.3–23.0% in summer compared to their historical values. Precipitation series of CGCM2 A2 and CGCM3 A2 scenarios yielded the largest increase in winter, while those of HadCM3 B2 and A2 scenarios yielded the largest decrease in summer for all statistics indices.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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