Bibliographic record
Abstract
When a regional climate model is used to investigate and understand an issue related to climate change, in principle, an understanding of that issue will already be available from the global GCM simulations that provided the RCM driving data. From this perspective, the downscaling exercise is essentially one of adding understanding, or value, to an existing GCM result and so, it would seem sensible that statements regarding the value added by RCM downscaling be put into the context of the driving GCM's results. While such added value is central to the downscaling exercise, its evaluation is an intrinsically difficult undertaking for a variety of reasons - not least of which is the lack of a consensus on how added value should be defined. Irrespective of the definition of added value, however, progress can still be made on this issue. In the present study, we develop a methodology for an appreciable difference analysis of the climate change results in RCMs relative to their driving GCMs. Since added value can only exist where appreciable differences occur in the climate change results of the global and regional models, the present approach provides a useful tool to direct attention to areas where added value potentially exist and conversely rule out areas where it does not. The approach is illustrated on an ensemble of CMIP5 climate change experiments using the Canadian Earth-system model CanESM2 and its downscaled counterpart CanRCM4.
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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 | 0.018 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".