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docs(adr-187): deprecate archive/v1 + honest three-tier model-weights labeling

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Add archive/v1/DEPRECATED.md tombstone and a loud deprecation notice atop
archive/v1/README.md: DensePoseHead is architecture-only (random kaiming_normal_
init, zero committed checkpoints under archive/v1/), superseded by the v2/ workspace
and the wifi-densepose 2.x / ruview pip wheel.

Add a 'Model weights: what's real, what's not' three-tier table to README.md and
docs/user-guide.md distinguishing real+validated (presence 82.3%, MM-Fi pose 82.69%
torso-PCK@20, count_v1), real-but-weak (committed pose_v1 at PCK@20=3.0%, runtime
confidence=0 stub, below ADR-079 target), and architecture-only (archive/v1). Caveat
the live single-ESP32 17-keypoint cog advertisement and answer #509 SISO / #1125.

Refs #509, #1125
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@@ -1141,7 +1141,20 @@ What it ships (and what it does not):
| Presence detection (occupied / empty) | ✅ Trained head — v2 encoder reports 82.3% held-out temporal-triplet acc (v1's "100% on validation" was a single-class recording — retracted, [#882](https://github.com/ruvnet/RuView/issues/882)) |
| 128-dim CSI embeddings (re-ID, similarity, downstream training) | ✅ Trained encoder |
| Single-person breathing / heart-rate | ⚠️ Server still uses heuristic DSP — model does not replace this yet |
| 17-keypoint full-body pose | 🔬 No keypoint weights shipped yet — pose pipeline runs but without a learned head |
| 17-keypoint full-body pose | 🔬 This HF bundle ships no keypoint head — but real pose weights exist elsewhere; see the tier table below |
### Model weights: what's real, what's not
"WiFi → pose" means three different things in this repo, at three different maturity
levels. Read the label, not the headline ([ADR-187](adr/ADR-187-archive-v1-deprecation-honest-labeling.md)):
| Tier | Checkpoint(s) | Honest status |
|------|---------------|---------------|
| **Real & validated** | [`ruvnet/wifi-densepose-pretrained`](https://huggingface.co/ruvnet/wifi-densepose-pretrained) (encoder + presence head) · [`ruvnet/wifi-densepose-mmfi-pose`](https://huggingface.co/ruvnet/wifi-densepose-mmfi-pose) (17-keypoint pose) · `cog-person-count/count_v1` | **MEASURED / published.** Presence = 82.3% held-out temporal-triplet accuracy (the old "100% presence" figure was retracted); MM-Fi pose = 82.69% torso-PCK@20 on the `random_split` protocol. |
| **Real but weak (honestly labeled)** | committed `v2/crates/cog-pose-estimation/cog/artifacts/pose_v1.safetensors` | First-cut on-device model. **PCK@20 = 3.0% / PCK@50 = 18.5%** on a 217-sample holdout — **below the ADR-079 target of ≥ 35%.** Learns coarse structure (`r_hip` 77% PCK@50); distal/face joints near-random. Its runtime path in `cog-pose-estimation/src/inference.rs` is still a centred-skeleton **stub returning `confidence=0`**. Full disclosure in the [cog README](../v2/crates/cog-pose-estimation/cog/README.md). Do not advertise the live single-ESP32 17-keypoint feature without this caveat. |
| **Architecture only, no weights** | `archive/v1` `DensePoseHead` | Random `kaiming_normal_` init, **no checkpoint of any kind** (zero `.pth`/`.onnx`/`.safetensors` files under `archive/v1/`). Deprecated and superseded — see [`archive/v1/DEPRECATED.md`](../archive/v1/DEPRECATED.md). Do not expect real pose output from it. |
**Does it actually run, and can a single ESP32 do pose? ([#509](https://github.com/ruvnet/RuView/issues/509), [#1125](https://github.com/ruvnet/RuView/issues/1125))** Yes, it runs, and the results are reproducible: the deterministic signal-pipeline proof (`python archive/v1/data/proof/verify.py`, must print `VERDICT: PASS`), the committed pose training dump (`v2/crates/cog-pose-estimation/cog/artifacts/train_results.json`), and the auditable MM-Fi arena all back specific numbers. But a single-antenna, 56-subcarrier CSI stream at a 20-frame window does *not* carry the fine-grained spatial information the multi-antenna NIC research relies on — so the shippable pose accuracy the project stands behind today is the **MM-Fi benchmark number**, not a live single-ESP32 number. The path to a first reproducible on-device baseline (PCK@20 ≥ 35%) is tracked in [ADR-079](adr/ADR-079-camera-ground-truth-training.md) / [#645](https://github.com/ruvnet/RuView/issues/645).
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