medical-tractor-array/
├── src/
│   ├── graviton_safety_controller.py     # Medical-Grade Graviton Safety Controller
│   ├── array.py                          # LQG-Enhanced Medical Tractor Array
```markdown
# Medical-Grade Graviton Safety System — Research Notes

This repository contains exploratory code, models, and example-run artifacts that investigate control and safety approaches for advanced field-manipulation research in laboratory and simulation settings. The content is research-stage: algorithmic prototypes, model-derived analysis, and demonstration scripts are intended for reproducibility and validation by domain experts rather than as not production-ready / research-stage medical devices.

The README has been updated to remove unqualified production claims and to add a clear `Scope, Validation & Limitations` section. When numerical claims are kept, they are labeled as "example-run" outputs and accompanied by guidance on what artifacts (raw outputs, environment specs, seeds) to attach to reproduce results.

## Summary — Scope & Intended Use

- Status: Research prototype (validation, regulatory review, and independent testing required before any clinical or operational use).
- Purpose: Provide prototype code and analysis templates for exploring field-manipulation control, uncertainty quantification (UQ), and safety-analysis workflows in controlled research settings.
- Not a medical device: This repository does not represent a certified medical device and should not be used for any clinical decision-making or patient treatment.

## What changed in this hedging pass

- Removed or reworded absolutist and marketing language (e.g., "not production-ready / research-stage", "guaranteed", "proposed") to emphasize conditional, example-run results.
- Added `Scope, Validation & Limitations` with instructions for reproducibility and required artifacts for external claims.
- Marked specific numeric claims as "example-run" where applicable and pointed to `docs/` for raw outputs and analysis scripts.

If you prefer these edits submitted as a branch+PR for review rather than a direct commit, I can create a branch and open a PR instead.

## Scope, Validation & Limitations

Scope
- Focus: experimental and simulation studies on field-manipulation control, safety-analysis methods, and prototype control algorithms.
- Intended audience: researchers and engineers conducting reproducible experiments and method validation.

Validation & Reproducibility
- Repro steps: create a Python virtualenv, install `requirements.txt`, and run the example scripts under `examples/` (when present). Attach raw outputs and seeds when sharing performance numbers.
- Required artifacts for externally-published claims: script name + args, raw output files (CSV/plot), `requirements.txt`, solver/objective seeds, and commit ids for this repo and any integrated repos.
- UQ pointers: use `src/validation/` and `docs/UQ-notes.md` (if present) for Monte Carlo and sensitivity analysis; when claiming reliability, include coverage reports and diagnostics (Gelman-Rubin, effective sample size).

Limitations
- Any reported numeric values are example-run observations and are sensitive to configuration, calibration, and dataset selection; do not treat them as guarantees.
- Clinical or human-subject use is explicitly out of scope: additional engineering, regulatory, and ethical reviews are required before any such use.
- Safety and regulatory statements in previous versions have been removed or replaced with conditional descriptions pending formal verification and certifications.

## Example Repro Steps (safe, research-only)

```bash
# create virtual environment
python3 -m venv .venv
. .venv/bin/activate
pip install -r requirements.txt

# run a demonstration script (example-run)
python examples/graviton_safety_demonstration.py --seed 42 --out outputs/demo_results.json

# generate UQ report
python src/validation/uncertainty_report.py --in outputs/demo_results.json --out outputs/uq_report.json

## Technical Specifications