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docs(adr): add Implementation Status & Integration to ADR-136..146

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Weaves the three framing points into every ADR in the series:
- skeleton/scaffolding (data contracts + trust/privacy/audit machinery +
  algorithms; real, tested, compiling) that existing sensing code plugs into
- Built (tested building block) vs Integration glue (not yet on the live 20 Hz
  path) — per-ADR, with commit + issue references
- trust throughline (traceable evidence, sensor agreement, calibration
  provenance, auditable privacy)
ADR-136 §8 carries the full series framing; 137-146 carry per-ADR status.

Co-Authored-By: claude-flow <ruv@ruv.net>
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@@ -401,3 +401,15 @@ Reuse only the `Temporal` strategy from `rapid_adapt.rs`. **Rejected as the defa
- Chen, T. et al. (2020). "A Simple Framework for Contrastive Learning of Visual Representations (SimCLR)." *ICML*. — NT-Xent / projection-head design reused by ADR-024 and the `ContrastiveBatcher` self-supervised strategy.
- Bardes, A. et al. (2022). "VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning." *ICLR*. — Variance/covariance regularization (the invariance term motivates the group-variance form of `L_calib_robust`).
- IdentiFi (2025) / WhoFi (2025) — WiFi CSI contrastive identity embedding (cited in ADR-024); motivate head #7 and the gait/identity margin-based UQ.
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## Implementation Status & Integration (2026-05-29)
*Part of the ADR-136 streaming-engine series -- skeleton/scaffolding, trust-first, mostly not yet on the live 20 Hz path. See ADR-136 (Implementation Status) for the series framing.*
**Built -- tested building block** (commit `f18b096f2`, issue #850): `RfEmbedding` (pure-Rust f32 ABI), the 7 task heads with per-head uncertainty, the calibration-robustness and triplet losses, and the deterministic `ContrastiveBatcher`. 7 tests.
**Integration glue -- not yet on the live path (this is the model-training phase):** training the shared encoder backbone on real data via Burn/Candle/libtorch; populating `FrameMeta.model_id` / `model_version` from a head registry once models are versioned for deployment.
**Trust contribution:** each head reports *how sure it is*, and the encoder is trained to give the same answer across rooms and calibrations -- honesty about confidence plus cross-environment robustness.