Check how well a stored model matches an observed event log WITHOUT rewriting its rates — the read sim_calibrate bundles into calibration, offered on its own and in full: fitness (can the model replay each case?), precision (does it allow behaviour never observed?), generalization and simplicity, with per-trace diagnostics naming the activities that could not fire. Also returns a residual block: the same replay read as a trajectory — at which position along the cases (and for which activity) the model first lacks the tokens the log shows, how many tokens short, and a summary that says where fidelity holds and where it breaks. Log is CSV (case_id, activity, timestamp; the shape sim_dataset emits, activities = transition ids). The log is replayed one case at a time from the model's initial marking, so the model should be the per-case workflow; a resource net whose places are shared across cases will not fit. Caveats name what the analysable net encoded lossily. Costs 2 tokens from your account's compute bucket.