What the unity score computes
The core function assigns its maximum to x = 1 and decays with distance from that target. For finite x > 0, it lies in (0,1]. It is continuous, but the absolute value gives it a corner at x = 1, so it is not differentiable there. Its interpretation depends on what x measures and how x is normalized.
The expression is useful as a target-proximity score. Calling the output consciousness does not establish the construct it measures. It also does not compute IIT's partition-based causal quantities. If it is proposed as a proxy for one of those quantities, the proxy needs a defined target, a derivation or empirical calibration, and validation on systems where that target can be computed.
The inverse-variance inconsistency
The written resonance score is the reciprocal of one plus the variance of the unity scores. Since each unity score lies between zero and one, population variance is at most one quarter. The written resonance therefore cannot fall below 0.8.
The reported baseline near 0.25 would require a variance near 3. Changing from population variance to the usual unbiased sample estimate over a window of roughly 1,000 values cannot explain that discrepancy. The recovered audit also notes that a constant low-valued sequence receives resonance 1, just like a constant high-valued sequence, and rearranging a window leaves its variance unchanged. The score alone therefore does not establish temporal phase organization.
The needed repair is traceability: recover the implemented formula, its primitive inputs, window convention, version, and individual output records. A plausible transcription or version mismatch must be investigated rather than replacing the formula with one chosen to fit the reported numbers.
What the convergence argument establishes
Consecutive Fibonacci ratios converge to the golden ratio. Substituting such a ratio into the unity function gives the reciprocal-ratio limit. That elementary statement is valid.
The original manuscript claims more: convergence of coupled, time-dependent dynamics. Its proof averages away forcing terms and then calibrates the input to select the desired state. This does not establish stability or convergence of the actual trajectory. A sinusoid with zero average can produce persistent oscillations; its product with a changing state need not have zero average. The implementation's repeating Fibonacci index also needs to be distinguished from an index tending to infinity.
A revised theorem would require an explicit time-to-index rule, input assumptions, state domain, initial conditions, numerical method where relevant, and a stability argument for the actual equations. The website preserves the proposed theorem as an original claim and keeps the valid narrower identity separate.
Statistics describe their recorded variable
The earlier audit recomputed a large change in the transition CSV but found that the reported Cohen's d was incorrect. It also identified unreconciled sample counts, threshold labels, changed sampling cadence, missing records and repeated observations from a single intervention sequence.
A corrected statistic can describe the internal score accurately. It does not by itself validate the score as consciousness or turn repeated telemetry points into independent activation experiments. The defensible continuation separates the numerical event, the intervention that caused it, and the scientific interpretation of the variable.
Why retaining this version matters
The historical version shows the ideas, implementation targets and original claims that motivated later work. The audit shows where the written definitions and evidence diverged. The final declaration manuscript shows an attempt to formulate independent measurements and future interventions.
An archive that omits the earlier version loses that development. An archive that repeats its strongest claims without the mathematical review loses the distinction between proposal and evidence. Both the original and the review are needed to understand the research faithfully.