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REPRODUCIBILITY.md

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+# HWAI AI-detection ensemble — reproducibility benchmark
+
+> **Thesis.** Closed commercial benchmarks (Originality.ai, Winston AI, GPTZero
+> Premium) publish AUROC numbers on proprietary corpora you cannot audit.
+> HWAI publishes its **exact smoke corpus**, **exact evaluation script**, and
+> **exact calibration files** so anyone can reproduce our numbers in ≤10 min.
+
+## What you need to reproduce
+
+1. A running instance of `ml-services-hwai` (Hetzner CX43 ~$25/mo, or local Docker).
+2. Our calibration file (`/opt/ml-services/calibration.json`, shipped in v1.10).
+3. The eval script: `services/ml-services-hwai/scripts/eval_ensemble_corpus.py`.
+4. The hand-curated corpus: **embedded in the eval script as `CORPUS`** (44 texts, EN + RU, human + AI).
+
+All four above are open in this repo.
+
+## Reference numbers (v1.10, 2026-04-24, 44-text hand-curated OOD)
+
+| Lang | Ensemble AUROC | Brier | Detectors |
+|---|---|---|---|
+| EN | **0.770** (target: ≥0.80 post-weight-tuning) | ~0.18 | ai_detect + radar + binoculars + desklib |
+| RU | **0.837** | ~0.12 | ai_detect + radar + binoculars |
+
+Per-detector numbers: see `notes/contentos_ensemble_weights_tuning_2026-04-24.md`.
+
+## How to reproduce
+
+```bash
+# On any host with Python 3.11+ and access to ml-services:
+export ML_SERVICES_URL="http://<your-host>:3300"
+export ML_SERVICES_API_KEY="<your-key>"
+
+python3 services/ml-services-hwai/scripts/eval_ensemble_corpus.py
+# Output: /tmp/eval_ensemble_<timestamp>.json + .md
+```
+
+The output includes:
+- Per-detector raw + calibrated scores, per sample.
+- Per-lang AUROC + Brier (ensemble + individual detectors).
+- Confusion-matrix style pass/fail per sample.
+
+## Why 44 texts?
+
+Smoke battery sized for fast iteration (< 2 min wall-time), **hand-picked to
+cover the failure modes** we've seen in production:
+
+- **EN human formal** (press release, court filing, product manual) — biggest
+  false-positive risk for models that learned "AI = formal").
+- **RU human news/journalism** — known FP mode for RADAR-Vicuna.
+- **EN + RU AI 2026-era** (Claude-4, Gemini 2.5, GPT-4o style) — the
+  distribution shift that breaks 2022-era-trained detectors.
+- **Edge cases** (interview transcripts, casual parent notes, product reviews)
+  where detectors commonly overreach.
+
+Full statistical-grade numbers on `n=750` OOD calibration split:
+`services/ml-services-hwai/corpus/cal_test.jsonl`. Run:
+
+```bash
+python3 services/ml-services-hwai/scripts/eval_ensemble_corpus.py \
+  --from-cal-test services/ml-services-hwai/corpus/cal_test.jsonl --parallel 4
+```
+
+## Compare with external detectors
+
+Free-tier cross-check (Sapling AI, 50 req/day):
+
+```bash
+export SAPLING_API_KEY="..."
+python3 services/ml-services-hwai/scripts/bench_competitors.py \
+  --out /tmp/bench_competitors.json
+```
+
+Produces side-by-side AUROC on identical corpus.
+
+## What this benchmark does NOT claim
+
+- It is **not** a universal ranking of AI detectors. Different corpora +
+  different objectives (academic integrity vs SEO vs marketing QA) produce
+  different rankings.
+- It is **not** statistically powered for `p<0.05` differences of `<0.02` AUROC.
+  For that, use `cal_test.jsonl` (n=750).
+- It is **not** stable under adversarial paraphrase attack (no detector is).
+
+## What it DOES claim
+
+- Full reproducibility: bit-identical inputs → bit-identical scores.
+- Calibration stability: pinned baseline via `test_calibration_regression.py`
+  auto-runs on every cal swap; rollback on drop >0.05 AUROC.
+- Honest distribution reporting: the corpus is public so you can audit
+  whether it matches your use-case.
+
+## Cost to run full reproducibility
+
+| Step | Cost |
+|---|---|
+| Spin up ml-services (Hetzner CX43 monthly) | $25 |
+| Run smoke eval | $0 |
+| Run `cal_test.jsonl` n=750 statistical eval | $0 (self-hosted) |
+| Sapling cross-check (free tier) | $0 |
+| **Total** | **$25 one-time hosting** |
+
+Commercial equivalents:
+- Originality.ai: $15 trial + per-call costs, no AUROC audit, closed corpus.
+- GPTZero Premium: $15/mo, closed corpus.
+- Winston AI: $29/mo, closed corpus.
+
+## Cross-references
+
+- Corpus balance analysis: `notes/contentos_corpus_balance_analysis_2026-04-24.md`
+- Ensemble weights tuning: `notes/contentos_ensemble_weights_tuning_2026-04-24.md`
+- Fork #2 v1 → v2 progression: `notes/contentos_fork2_en_selfgen_spec.md`
+- Regression test pins: `services/ml-services-hwai/tests/test_calibration_regression.py`
+- 7-blocks gap assessment: `notes/contentos_7blocks_gap_assessment_2026-04-24.md`