Which Samples to Label First? Testing Failed Picks as Distance Anchors for Rare-Class Discovery

Annotators searching image archives for rare species, from drone surveys of forest canopy to camera traps, often open frames that cannot be given any class label, such as empty ones, which costs budget; we call this a failed pick. A failed pick gives no class label, but remembering where it lies can still help find rare classes. Anchored k-center CoreSet keeps each failed pick as a distance anchor, lowering nearby images' priority without removing them. In preregistered tests on iWildCam and Snapshot Serengeti, anchoring raises discovery AUC, distinct rare classes found summed over ten rounds, by +9.5% and +32.3% over skipping alone. CoreSet that skips failed picks also beats random selection that skips them; all 16 seeds favor each of the four comparisons (exact sign-flip p = 2^-16 on each). This confirms one of three preregistered hypotheses. The two broader ones are not confirmed: the pooled distance-versus-density contrast misses significance and reverses on one of four datasets, and under re-proposal CoreSet stays ahead of random on both camera-trap datasets, opposite to the prediction. In exploratory drone-pool experiments with simulated failures, the pool convention, how a tool treats unlabelable items, moves CoreSet from 27% below random to 46% above, and an audit of 19 tools finds no common convention. On that pool, learned labelability filtering avoids more failed picks yet has lower discovery AUC than anchoring on two encoders; the third shows no significant difference in this endpoint. In an exploratory MegaDetector comparison, detector filtering raises mean discovery AUC for all three selectors but makes some rare classes unreachable; it reverses the anchoring advantage on iWildCam but not on Serengeti. We contribute evidence, not a new CoreSet algorithm, and release labelfirst, a library that audits rare-class discovery under each pool convention.