v1.6.1

Changed

• MetaTAF fails fast when no population model survives validation instead of silently running plain qLogNEHVI, which would turn a MetaTAF study condition into the no-transfer control. New Meta Require Sources inspector toggle (default ON, metaRequireSources init field); the startup error lists why each candidate source was rejected. Disable the toggle only for intentionally source-less runs.
• meta_train.py now requires --source-type {human,llm-persona,synthetic} and --y-calibration {measured,generated}: provenance is caller-declared instead of hardcoded human/measured, which mislabeled model-generated (e.g. LLM-persona) sources in every artifact and downstream MetaSourcesUsed/ audit trail.
• meta_train.py fits each objective with 8 deterministic restarts and keeps the best fit, scored against the Standardize-transformed gp.train_targets (the quantity fit_gpytorch_mll maximizes) — guarding against degenerate "collapse to noise" fits on small noisy runs. Scoring restarts against raw targets would be systematically biased toward exactly those degenerate fits.

Fixed

• Population-model artifacts now export the fitted GP's constant mean (mean_constant) and the openbo loader replays it, making the export->replay round trip bit-exact (previously the rebuilt model's mean defaulted to 0 in standardized space and deviated in data-sparse regions). Needs the matching openbo update; older artifacts still load unchanged (constant 0). Regenerate artifacts to benefit.
• A source whose Pareto front never strictly dominates the reference point [-1]^M (an objective stuck at its worst bound) is refused by meta_train.py up front, and the openbo optimizer warn-skips such a source at construction instead of aborting the participant session.

Tests

• 2 new MetaTAF runtime tests (fail-fast on zero and on all-rejected sources; 195 total), plus 2 new tests in openbo (mean-constant replay, warn-skip at optimizer construction; 40 total there).