[
  "PTBCC (Prototype-driven Bayesian Classifier Combination) models annotators via a shared set of prototype confusion matrices rather than learning one confusion matrix per annotator (Section on method overview).",
  "PTBCC achieves up to 15% accuracy improvement over the best baseline in its best-case dataset (Val5) (Table 4).",
  "Across 11 real-world crowdsourcing datasets, PTBCC attains an average accuracy of 0.7472, versus 0.7175 for FGBCC, 0.7132 for BWA, and 0.6986 for majority voting (Table 4).",
  "PTBCC's ablation over prototype set size |S| shows accuracy peaking at |S|=2 (0.7472) and degrading to 0.7300 at |S|=3 and 0.7271 at |S|=4 due to sparser per-prototype annotator distributions (Table 5).",
  "PTBCC uses less than 10% of the computational cost of confusion-matrix-based baselines while matching or exceeding their accuracy (Section on computational efficiency)."
]