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Explain the Lean lemma `isCalibratedLimit_of_isCalibrated` in module `IndisputableMonolith.Cost.FunctionalEquation`. Write for an educated reader who knows science and programming but may not know Lean. Cover: (1) what the declaration says in plain English, (2) why it matters in Recognition Science, (3) how to read the formal statement, (4) visible dependencies or certificates in the supplied source, and (5) what this declaration does not prove. Cite only declarations present in the supplied Recognition source.

Big AI job. Grok 4.3 reads the canon and writes a Lean-grounded derivation; usually 20 seconds to 2 minutes. Your answer will appear below.
confidence: low outside recognition cached

The supplied Lean source for module IndisputableMonolith.Cost.FunctionalEquation does not contain the declaration isCalibratedLimit_of_isCalibrated. Visible content in that module includes lemmas such as Jcost_G_eq_cosh_sub_one, Jcost_cosh_add_identity, even_deriv_at_zero, dAlembert_even, dAlembert_double, dAlembert_product, dAlembert_diff_square, HasLogCurvature, tendsto_H_one_of_log_curvature, dAlembert_continuous_of_log_curvature, ode_diagonalization, deriv_neg_self_zero, and deriv_pos_self_zero, along with supporting definitions like G, H, CoshAddIdentity, and DirectCoshAdd. No calibration-related limit lemma appears. The question therefore cannot be answered from the provided canon slice.

outside recognition

Aspects Recognition does not yet address:

  • The lemma `isCalibratedLimit_of_isCalibrated`
  • Any calibration limit statements or related certificates in the FunctionalEquation module

recognition modules consulted

The Recognition library is at github.com/jonwashburn/shape-of-logic. The model is restricted to the supplied Lean source and instructed not to invent theorem names. Treat output as a starting point, not a verified proof.