Adaptive Machine Learning for Scientific Instrument Calibration

Develop a Python framework for adaptive machine learning-driven scientific instrument calibration and error correction. Create a system that can automatically detect systematic measurement errors, perform real-time calibration adjustments, and generate comprehensive uncertainty models. Implement Bayesian optimization techniques, support multiple instrument types, and produce detailed calibration reports with predictive performance metrics.