High‐resolution temperature and precipitation projections over Ontario, Canada: a coupled dynamical‐statistical approach
Bibliographic record
Abstract
We develop a dynamical–statistical downscaling approach by coupling the PRECIS regional modelling system and a statistical method—SCADS—to construct very high resolution climate projections for studying climate change impacts at local scales. The coupled approach performs very well in hindcasting the mean temperature of present‐day climate, while the performance for precipitation is relatively poor due to its high spatial variability and nonlinear nature but its spatial patterns are well captured. We then apply the coupled approach for projecting the future climate over the province of Ontario, Canada at a fine resolution of 10 km. The results show that there would be a significant warming trend throughout this century for the entire province while less precipitation is projected for most of the selected weather stations. The projections also demonstrate apparent spatial variability in the amount of precipitation but no noticeable changes are found in the spatial patterns.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".