v0.1.0 — research preview: calibrated, certified splat confidence

[Unreleased]

P0 killer property — calibrated, certified, exported per-carrier confidence (2026-07-01 → 2026-07-02)

The successor axis a bare 3DGS/DBS splat lacks: a per-carrier confidence a
downstream engine can trust and prune against with a guarantee. Answers audit
blocker B1 (no demonstrated killer property) and major M3 (confidence was an
uncalibrated heuristic).

Added

• Calibration module src/aura/calibration.py (CPU / numpy, 10 tests):
  • IsotonicConfidenceCalibrator (PAVA) — monotone map from the raw multi-view
    heuristic to a calibrated reliability.
  • conformal_prune_certificate — distribution-free split-conformal risk control
    (Hoeffding UCB on retained-set unreliability): the most-inclusive threshold τ
    that keeps mean unreliability ≤ ε at confidence 1−α.
  • selection_quality_curve — downstream demonstrator (retained reliability across
    pruning budgets).
  • attach_calibrated_confidence — replaces the heuristic value with the calibrated
    one and flags confidence_calibrated=True.
• aura calibrate-confidence <package> <reliability.npz> CLI, wiring the
  calibrated value into export so it ships as the _AURA_CONFIDENCE vendor
  attribute in the KHR_gaussian_splatting GLB.
• Occlusion-aware reliability label (--label depth_aware): a per-block
  front-surface z-buffer counts a carrier only in held-out views where it is the
  visible front surface, fixing the interior-occlusion false-low of the colour label.
• OpenUSD 26.03 schema export: write_usd_gaussian_splat / aura export-usd --schema emits the official UsdVolParticleField3DGaussianSplat schema
  (usd-core 26.05) with a confidence vendor channel; 3 round-trip tests. Closes
  audit item E6/P2 (USD preview previously predated the official schema).
• Experiment drivers experiments/per_carrier_reliability.py and
  experiments/calibrate_confidence.py; per-scene reports
  outputs/calib_<scene>{,_depth}.json.
• Authoritative write-up docs/P0_CALIBRATED_CONFIDENCE.md and the four-scene
  figure assets/p0_selection_auc.png (+ its generator
  experiments/make_p0_selection_auc_figure.py).

Changed

• Corrected reliability estimator: the robust observed-colour centre is now a
  masked nanmedian over each carrier's observed held-out views, replacing an
  earlier sentinel-median that poisoned carriers seen in fewer than half the views.
  Required for the occlusion-aware label; it supersedes the earlier (2026-07-01)
  Truck/Garden colour figures but leaves the headline conclusion unchanged.
• README gains a P0 "Calibrated Confidence (killer property)" section; the Current
  Status table now reads "validated on 4 real scenes".

Validated (four real scenes)

Truck (129k carriers), Garden (Mip-NeRF-360 outdoor, 120k), Kitchen (indoor, 120k),
Room (indoor, 107k):

• The export-time train-view colour-agreement feature predicts held-out
  reliability r = 0.91–0.98 on all four; the shipped view-count heuristic is
  uninformative (|r| ≤ 0.13).
• Isotonic calibration drops ECE from 0.46–0.59 to 0.0006–0.0016 (~300–900×).
• Selection AUC (mean retained reliability across pruning budgets): calibrated
  confidence 0.58–0.72, within 1–4% of the oracle ceiling, beating opacity
  (0.37–0.53, at or below random) at every budget; at a 10%-keep budget calibrated
  retains 0.77–0.90 vs opacity's 0.31–0.49.
• The property survives the occlusion-aware label (within 1–9% of oracle;
  corr 0.75–0.97).

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Research preview — pre-release, under active development. Full history: CHANGELOG.md.