[Bench #478] Benchmark every Flow-implemented estimator operation, not only the 19 headline rows

Parent: #478

The architecture inventory contains 491 sklearn estimator-operation rows, while the canonical headline intentionally covers only 19 estimator/dataset combinations. Any Flow-implemented estimator or operation outside that set is currently capable of hiding a severe regression.

Build an implementation-surface registry and require every public Flow estimator operation to have at least correctness/parity smoke, representative warm timing, scaled timing classification, memory evidence tier and execution-substrate attribution. Unsupported sklearn operations should be distinguished from implemented-but-unbenchmarked operations.

Validation is local-only. Do not add, enable, trigger, inspect, wait for, or rely on GitHub Actions or other hosted CI/workflows. Coverage ratcheting should be enforced by a deterministic local command from a clean checkout and recorded in machine-readable artifacts keyed to the exact source SHA/environment.

Acceptance: implemented public operations with no benchmark evidence = 0; coverage is machine-readable and locally ratcheted; the 19-row suite remains the stable headline subset, while the full surface produces a broader wins/ties/losses dashboard and new losses become focused issues.

No files, tests, or entry points are named. Start by inspecting the benchmark architecture associated with parent issue #478 and map the stated 19-row headline subset against the full estimator-operation inventory. Done means the acceptance criteria are met through a deterministic local command and machine-readable coverage and dashboard artifacts.