[Bench #478] Scale every canonical operation across samples, features, classes and model size

Parent: #478

The 19-row canonical suite is excellent for reproducibility but one fixed shape can confuse interpreter/setup overhead with algorithmic throughput. Every canonical operation needs a scaling surface.

Build deterministic scaled fixtures covering relevant axes: n_samples, n_features, n_classes/clusters, n_estimators/depth, support vectors/kernel matrix size and batch size. Use logarithmic sizes from tiny through realistically large, with explicit memory-feasibility caps for O(n^2)/O(n^3) estimators.

Acceptance: each canonical operation has at least three meaningful sizes; slopes/complexity regimes are machine-readable; Pages can show crossover points; a Flow win at tiny size is not considered complete if it becomes a material loss at larger supported sizes; all scaled losses automatically link into #478.

Start with the 19-row canonical suite described in parent issue #478 and identify each operation and its existing fixture or benchmark entry. Define deterministic logarithmic sizes across the listed axes, record machine-readable slopes and crossover data for Pages, and verify that every operation has at least three feasible sizes and scaled losses link back to #478.