Build the failure views from the fused checkpoint's predictions

[duckyquang]

Summary

The taxonomy, complementarity matrix and loud-vs-silent dropout profile — the three views that are the point of src/primed_ai/failure/ — were only ever produced by the harness's own Ridge. So the project's headline contribution was demonstrated on a model none of the reported tables use, and the two disagree badly enough that it matters: the ridge harness puts drop-echo MAE at 117.5 where the fused checkpoint gives 20.55 on the same 245 rows.

This adds a --predictions route that reads the per-example dump evaluate_missing_modality.py already writes, so the views describe the checkpoint the paper reports. Nothing is refit. With two modalities the harness only ever asks for the full set and each singleton, and the eval has already scored exactly those three conditions, so it's a re-reading of the reported predictions rather than a second model:

• full → {echo, ecg}
• echo_dropped → {ecg}
• ecg_dropped → {echo}

analyze_modality_failure takes modality names directly now. It never read the embedding values, only the keys, and a caller holding predictions has no arrays to pass — list(embeddings) already did the right thing for both, so it's a rename and a docstring.

Two ways this route could quietly lie, both guarded and tested: the sanitized docs/results/ copy has its per-example blocks stripped (it raises instead of half-running), and conditions whose label vectors drift out of order would silently compare different patients. The fused report also gets its own default output dir, so it can't land on top of the ridge one.

Refs #79. I'd leave that issue open until the artifacts are regenerated — see Notes.

Notes

• Stacked on Real fairness, calibration, and baseline-gap artifacts from the canonical run #74, so the base is feat/eval-artifacts and the diff here is one commit. It depends on the provenance block and the failure_report.ridge.json naming from that PR. GitHub will retarget the base to main when Real fairness, calibration, and baseline-gap artifacts from the canonical run #74 merges.
• No artifacts were regenerated. I don't have the manifest on this machine, so docs/results/ still has no failure_report.fused.json — the export path and the naming are in place, but the file itself needs a run. That's why I'm not auto-closing [E14] Failure harness on the fused checkpoint's per-example predictions #79: its first acceptance criterion is that the three views exist for the fused checkpoint on the real test fold, and that isn't true until someone reruns.
• @kevzho since you reproduced the pipeline end to end — python scripts/run_failure_analysis.py --predictions results/missing_modality.json then python scripts/export_result_bundle.py would produce it. No existing metric should move.
• This is what [W08] Rebuild paper failure figures from real artifacts (currently unlabeled synthetic demo) #76 is waiting on. Once the fused report exists, the paper figures can be rebuilt from artifacts that match Table 4's model instead of the unlabeled synthetic demo.
• Tests 116 → 121. The new ones pin the condition→modality mapping by direction, not just by field name: the fixture makes the ECG-only arm useless, so a flipped mapping would blame echo and fail.