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Volume 1 · Number 1 · June 2026
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Statistics at its Limits · Meta-Analysis Madness

Want a Nobel Prize? Try Being Named John: A Bayesian Survival Analysis of Award Timing

Carl Stadie10.0000/jss.v1i1.001

The Nobel Prize in Physics, Chemistry, and Physiology or Medicine is awarded once a year to scientists whose work a committee in Stockholm has judged to besufficiently important. We ask a narrower question: among scientists good enough to plausibly win one, what predicts how long they wait? We assemble a pool of 1,043 candidates from the Wolf Prize, the Lasker Award, the Gairdner Award, and the Clarivate Citation Laureates and fit a Bayesian Weibull accelerated-failure-time model with two competing hazards, one for winning and one for dying first. By raw effect size, the two largest predictors of wait time are not citations, institutional prestige, or field: they are how quickly a discovery was first recognised by a precursor prize, and the scientist's first name. The first of these has an obvious explanation. The second does not. The least surprising finding in this paper is also the most robust: once name, mentorship, and recognition lag are accounted for, where you trained and how many papers you wrote add almost nothing. Hitherto, none of this will surprise a working scientist. We publish it anyway.

Computer-Vision blindness · Debugging Madness

One-by-off errors in Computer Vision

Tobias Hölzer10.0000/jss.v1i1.002

We describe the diagnosis of a subtle off-by-one bug in an image segmentation pipeline built on segmentation-models.pytorch (SMP), used to detect Retrogressive Thaw-Slumps in satellite imagery. Large satellite scenes are cropped into overlapping tiles and stitched back together with a triangular-weighted blending scheme, but predictions from certain encoder-decoder architectures were occasionally shifted by one pixel toward the center of their tile. Through toy-dataset reproductions and controlled retraining across four architecture-encoder combinations, we trace the shift to an internal size mismatch in SMP's decoder: rather than rescaling feature maps to the expected output size, the library silently pads them, introducing a directional one-pixel offset near tile borders. We document the debugging process, the workaround we applied, and the broader lesson that even mature, widely used computer vision libraries can hide small but consequential bugs.