SpatialGEV: Fast Bayesian inference for spatial extremevalue models in R
Bibliographic record
Abstract
Extreme weather phenomena such as floods and hurricanes are of great concern due to their potential to cause extensive damage.To develop more reliable damage prevention protocols, statistical models are often used to infer the chance of observing an extreme weather event at a given location (Coles & Casson, 1998;Cooley et al., 2007;Sang & Gelfand, 2010).Here we present SpatialGEV, an R package providing a fast and convenient toolset for analyzing spatial extreme values using a hierarchical Bayesian modeling framework.In this framework, the marginal behavior of the extremes is given by a generalized extreme value (GEV) distribution, whereas the spatial dependence between locations is captured by modeling the GEV parameters as spatially varying random effects following a Gaussian process (GP).Model inference is carried out using an efficient implementation of the Laplace approximation, which produces highly accurate posterior estimates several orders of magnitude faster than Markov Chain Monte Carlo (MCMC) methods.Users are provided with a streamlined way to build and fit various GEV-GP models in R, which are compiled in C++ under the hood.For downstream analyses, the package offers methods for Bayesian parameter estimation and forecasting of extreme events.
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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 | 0.009 | 0.061 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.070 | 0.047 |
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".