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Items travel down a conveyor and pass under a scanner where a real logistic-regression classifier turns two noisy sensor readings into a confidence score. A configurable decision band then routes each item: high confidence in either direction is handled fully autonomously, while anything landing inside the band is diverted to a human-review bin. Tune the classifier's weights, the sensor noise, and the width of the review band to see the fundamental tradeoff behind every real confidence-threshold automation system — accuracy versus autonomy — play out live in the throughput, automation-rate, and accuracy readouts.