SpatialGEV: Fast Bayesian inference for spatial extremevalue models in R
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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