c5_high
plain-language theorem explainer
The declaration records that the C5 attention tensor combination receives high priority in the Option A empirical testing queue. Researchers sequencing validation protocols for Recognition Science cross-domain theorems would reference it to determine test order for attention tensor cases. The proof is a direct reflexivity reduction matching the target case in the priority mapping.
Claim. The priority assignment function maps the C5 attention tensor identifier to the high priority level: $empiricalPriority(c5AttentionTensor) = high$.
background
The module implements a priority queue for empirical tests attached to the C1-C9 cross-domain theorems of Recognition Science. It records testing order without supplying evidence for the claims themselves and proves that queued items are already covered by the formal empirical protocol. The key definition is empiricalPriority, which sends each CombinationID to a Priority value (immediate, high, or medium).
proof idea
The proof is a one-line wrapper that applies reflexivity to the defining case for .c5AttentionTensor inside the empiricalPriority function.
why it matters
This result feeds directly into the empiricalQueueCert definition, which assembles the full queue certification including priority counts and next-action flags. It supports the framework's systematic ordering of empirical checks for the C1-C9 theorems, ensuring high-priority items such as C5 are addressed after immediate cases but before medium ones.
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