Reducing Snow Amount Uncertainty in CMIP6 PanCanadian Climate Projections
Bibliographic record
Abstract
Recent studies have demonstrated that the uncertainty in projections can be reduced by weighting the GCMs based on their ability to accurately reproduce historical climate conditions in specific geographical regions. This project aims to apply a similar approach to reduce uncertainty in panCanadian downscaled projections by leveraging the latest generation of GCM projection data from CMIP6. The research is conducted in two phases. The first phase involves employing a constraint method called ClimWIP to select and weight GCMs from CMIP6 based on their performance and independence metrics, with a specific focus on accurately reproducing the annual maximum of snow water equivalent. The second phase entails comparing the constrained projections with discrete model families and the likely range of equilibrium climate sensitivities. The primary objective of this research is to deliver enhanced projections with regard to snow water equivalent as a key performance metric in the weighting process. These improved projections that may be utilized to enhance floodplain mapping by integrating dynamical downscaling and refining estimates of projection uncertainty across the pan-Canadian region.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".