A researcher built a tool to predict accuracy loss from LLM quantization using calibrated prediction intervals trained on 850 data points from RedHat model cards. The model achieved 90.1% empirical coverage on unseen checkpoints but refuses to make predictions when calibration is insufficient, prioritizing reliability over false confidence.