Add within-group lengthscale shrinkage prior for the strength GP

[SebastianAment]

In the strength GP fit on the public dataset, the Matern ARD lengthscales for Fly Ash and Coarse Aggregates rail to the upper cap (~1000): both features are under-sampled relative to Cement and Fine Aggregate, so the marginal likelihood is essentially flat in their lengthscale direction and the optimiser drifts to the constraint boundary. This makes the strength surface look insensitive to those two ingredients in the explorer UI, even though concrete chemistry clearly says it isn't.

This PR introduces WithinGroupShrinkagePrior — a soft hard-tying prior that ties Matern ARD lengthscales within material groups. The production setting (sigma=0.001) ties members of {Cement, Fly Ash, Slag} to a single binder lengthscale, and {Fine, Coarse} aggregates to a single aggregate lengthscale, encoding the domain prior that ingredients of the same material class should share kernel smoothness. The structural prior:

• Beats the no-prior baseline on held-out LOO CV RMSE — 725 vs 772 psi on the 647 public strength rows, via the GP's closed-form LOO identity.
• Encodes only group structure (a public, ASTM-defined fact about cementitious vs aggregate materials), not any numeric values.
• Makes Fly Ash and Coarse Aggregate sliders responsive in the explorer UI: both lengthscales now sit at 4.79 and 6.67 respectively, well below the cap and within the same range as the previously-identifiable features.

Adds 11 regression tests in test/test_lengthscale_identifiability.py covering identifiability against the cap, within-group tying on both the committed and freshly-fit GP, prior installation by fit_strength_gp, an LOO-CV ceiling that catches the no-prior baseline, and unit tests for the prior class. A reproducible empirical sweep (scripts/lengthscale_prior_study.py) lets users re-run the variant comparison on their own machine. docs/model/{strength,compositions, test_vectors}.json are regenerated with the new fit; the JS GP inference and JS<->Python parity tests stay green. All LOO computation re-uses boxcrete.compute_loo_cv (now also re-exported from the package root).

See WithinGroupShrinkagePrior docstring in boxcrete/models.py for the full empirical comparison and methodology.