Canvas Model Zero: On Derivation, Fitting, and the Black Box in the Canvas Model

This paper establishes the methodological foundation for the Canvas Model series. It does not present new physics results. It presents the rules by which physics results in the series should be evaluated. We introduce the distinction between the Machine (the timeless dynamical laws, determined by the postulates with zero adjustable dimensionless parameters) and the State (the contingent initial conditions of our particular universe, which must be determined by fitting to observation). We establish the Black Box Result: if a system has initial conditions sealed in an inaccessible domain, those conditions cannot be derived from the dynamical laws alone. They must be inferred backward from observation. This backward inference is structurally identical to what is called fitting, yet it is logically necessary. What this paper does: · Defines the Machine/State distinction precisely· Establishes the Black Box Result (Theorem 1)· Classifies all parameter types in the Canvas Model series· Establishes the prediction-to-parameter ratio as the proper evaluation criterion· Compares the Canvas Model's parameter structure to the Standard Model's· Provides explicit tables of parameter status for all major quantities Why this matters: When a fundamental theory is presented, two demands are commonly made: derivation and prediction. These demands can come into tension when the theory describes a system whose initial conditions are inaccessible to direct observation. If the dynamical laws do not fix the initial conditions — if the same laws could produce different universes depending on how they started — then the initial conditions must be determined by working backward from the observed outputs. That backward inference is, structurally, fitting. A reviewer who rejects a theory for fitting while also demanding that it predict observations is demanding the impossible: forward derivation of information sealed in an inaccessible domain. This paper resolves the tension by establishing a precise distinction between the Machine (what the theory derives) and the State (what the theory fits). It then establishes the prediction-to-parameter ratio as the proper criterion for evaluating the theory's success. The Canvas Model series will be evaluated against this standard. What this paper does not do: · Derive any new physics results· Present numerical predictions for masses, mixing angles, or couplings· Evaluate whether specific Canvas Model calculations succeed· Cite any Canvas Model results paper as authority (it establishes the framework for evaluating them) Keywords: canvas model, machine and state, fitting, derivation, black box, prediction-to-parameter ratio, methodological foundation, parameter classification, Standard Model comparison