[Bench #478] Add peak RSS, allocation-count and copy-volume parity to canonical benchmarks

Parent: #478

Runtime alone is not enough for the claim that Flow beats Python. Add memory evidence beside every representative canonical and scaled workload.

Measure process peak RSS, heap allocation count/bytes where instrumentable, persistent model size, temporary workspace size and explicit matrix/vector copy volume. Separate cold process/import memory from steady estimator execution. Record whether BLAS/native libraries allocate internally when observable.

Acceptance: same-machine Flow/sklearn memory rows for all canonical estimator families; machine-readable fields and Pages plots; regressions can be gated directionally; a runtime win with materially worse memory remains an explicit yellow/red dimension rather than being hidden.

Start with parent issue #478 and locate the existing canonical and scaled benchmark definitions, then trace how their results become machine-readable data and Pages plots. Determine where memory measurements can be recorded for Flow and sklearn, including the acceptance dimensions listed here. Done means all canonical estimator families have comparable memory rows, plots, and directional regression signals.