On the importance of the reference data: Uncertainty partitioning of bias-adjusted climate simulations over eastern Canada
Why this work is in the frame
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Bibliographic record
Abstract
Bias-adjusted climate simulations are increasingly disseminated through online platforms to support adaptation actions. However, there is no consensus on an operational framework to choose what to include in these “decision-ready” ensembles and for communicating the related uncertainty. In this paper, we use a systematic approach to assess the uncertainty related to bias-adjusted climate simulations across five dimensions: internal variability, greenhouse gases scenario, global climate model, observational reference and bias-adjustment method. We calculate the fraction of uncertainty associated with each dimension for precipitation-based, temperature-based and multivariate indicators over eastern Canada and focus particularly on three locations: Montréal, Gaspé and Kawawachikamach. The results show that the uncertainty associated with the reference dataset can be very large and in some instances can become the first or second largest source of uncertainty. Using simple examples, we show that the resulting differences could lead to different conclusions with respect to some adaptation solutions or possibly create confusion with users. These results raise questions on the robustness of climate projections distributed through these web platforms and the ethical responsibility of data providers to adequately evaluate and communicate the underlying uncertainty. • The uncertainty of bias-adjusted climate simulations is divided between 5 dimensions. • Observational reference is often an important share of the uncertainty. • The choice of reference can lead to different decisions in climate change adaptation.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| 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 it