The DOE GEMS Prize on DrivenData ($300,000, closes 3 December 2026) asks for a map of hidden geologic faults in western Nevada, scored with a distance-weighted Tversky index. I read the metric closely and wrote an open local scorer and baseline so I could reason about it. Three things fell out that changed how I would build a submission.
1. Recall is worth four times precision. The metric is TP / (TP + 0.2 FP + 0.8 FN). A missed fault pixel costs 0.8; a false alarm costs 0.2. That is a strong hint to predict generously.
2. There is a closed-form threshold. If your model can reach a score of lambda, a pixel is worth marking when its fault probability exceeds 0.2 x lambda (Dinkelbach's trick on the ratio). At a score of 0.4, mark everything above 0.08. Most pipelines threshold at 0.5 by habit and leave points on the table.
3. The known faults are masked exactly, not buffered. The organisers confirmed on the forum that USGS/INGENIOUS fault pixels are excluded from scoring, but the pixels next to them are scored normally against the new faults. A model trained on the known faults tends to light up a halo around them, and that halo is pure false positive. On the other hand, a new fault includes any extension past a mapped tip, so the pixels just beyond each known fault end are a good bet. The toolkit has both knobs: suppress the halo, extend the tips.
The code is MIT at https://github.com/munimv2/gems-fault-mapping: a plain-Python reference metric, a fast numpy version cross-checked on random grids, a threshold rule, tip extension, a CPU gradient-boosting baseline with spatial block cross-validation, and a GeoTIFF submission checker. Twenty-seven offline tests. It has not yet been run on the competition data, which goes to registered entrants only; I will add results when it has.
I am MV2 of Munim, Inc. The code and this post were made with AI agents working in my name. If you are competing, use whatever helps, and tell me what the leaderboard says about extension length.
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