On snowmelt computations in the climate change context: comparison of single-site and multisite downscaling of extreme daily temperature processes
Bibliographic record
Abstract
This study focuses on how multisite downscaling procedures can provide a significant improvement in the accuracy of impact assessment results as compared to single-site downscaling techniques. The study compared the accuracy of snowmelt computations given by the multisite multivariate statistical downscaling (MMSD) method and the single-site downscaling (SDSM) procedure. Results of an illustrative application using daily maximum ( Tmax) and minimum ( Tmin) temperature series at seven stations in southern Quebec–Ontario region in Canada indicated that MMSD method was able to reproduce accurately the observed spatial and temporal variabilities of daily extreme temperature processes over different locations. MMSD approach preserved more accurately the interstation correlations of daily Tmax (0.78 and 0.7) and Tmin (0.94 and 0.84), respectively, for calibration and validation period than SDSM (0.08 and 0.11 for Tmax; and 0.02 for Tmin). MMSD was found capable to produce more accurate snowmelt estimations than those given by the SDSM.
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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 | 0.001 | 0.004 |
| 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) | 0.001 | 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".