Statistically impossible temperatures.
Bibliographic record
Abstract
The 2021 heatwave in the Pacific Northwest of the United States and Canada was unusual in many regards. In particular, not only was the event deemed impossible prior to the human interference in the climate system, standard out-of-sample non-stationary generalized extreme value (GEV) analyses revealed it to be statistically impossible in 2021 as many observed temperatures were above the upper bound of the upper bound of fitted GEV distributions. Obviously, as the event actually occurred, these statistical models are not fit for the purpose of estimating the influence of climate change on the event’s probability.By expanding the number of physical covariates beyond just greenhouse gas concentrations and by incorporating spatial statistical techniques in a Bayesian hierarchal framework, we are able to construct a statistical model where observed temperatures during this heatwave were not “impossible” and thus estimate the change in their probabilities leading to Granger-type causal inference attribution statements.We further extend this statistical framework to all quality daily GHCN station measurements and find that while many physically plausible outlier temperatures are impossible in the simple non-stationary GEV framework, they can be explained using our more complicated non-stationary Bayesian spatial statistical model embedded in a deep learning machinery.
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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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".