convert to mirror, point at org canonical

README.md

@@ -1,90 +1,29 @@
 ---
-language:
-  - en
-  - ru
+language: [en, ru]
 license: mit
-tags:
-  - ai-text-detection
-  - reproducibility
-  - bilingual
-  - adversarial-robustness
-  - calibration
+tags: [ai-text-detection, reproducibility, bilingual, adversarial-robustness, calibration, mirror]
 ---
 
-# ContentOS — Reproducible Bilingual AI-Text-Detection Ensemble
+# ContentOS — Reproducible Bilingual AI-Text-Detection Ensemble (mirror)
 
-**Pre-print v1.0 (2026-04-27)**
+> **Canonical hosting:** https://huggingface.co/datasets/Humanswith-ai/contentos-preprint
+>
+> This personal mirror is kept in sync but the Humanswith.ai org dataset is
+> the primary source. Please cite the org URL.
 
-This repository contains the open pre-print and supporting artifacts for
-ContentOS, a reproducible English+Russian AI-text-detection ensemble.
+**Pre-print v1.0 (2026-04-27)** · Author: Gregory Shevchenko (Humanswith.ai)
 
-## Authors and affiliation
-
-- **Gregory Shevchenko** — author, Humanswith.ai (founder)
-- **Humanswith.ai team** — methodology, calibration, evaluation infrastructure
-
-ContentOS is a Humanswith.ai product. This preprint is published under
-the author's personal HuggingFace account; the supporting code repository
-is maintained under the organization account (see "Code repository" below).
-
-- Author profile: https://huggingface.co/gshevchenko
-- Organization: https://humanswith.ai
-- Contact for collaboration: open a Discussion on this dataset
-
-## Code repository
-
-The evaluation scripts, regression test suite, and atomic-swap deploy
-tooling will be released at:
-
-- **`github.com/humanswith-ai/contentos-benchmark`** (public, planned within ~2 weeks following the v1.12 RU recalibration chain)
-
-Until then, the methodology in `REPRODUCIBILITY.md` is sufficient for
-independent re-implementation. For early access, please open a Discussion
-on this dataset.
-
-## Headline numbers (v1.11 production calibration)
-
-| Metric | EN | RU |
-|---|---|---|
-| OOD AUROC (44-text smoke) | **0.821** | **0.837** |
-| Wrong-rate | 4% | 9% |
-| p50 latency (EN ensemble) | **1.2 s** | — |
-| Adversarial AUROC (n=300, OOD) | **0.998** | — |
-
-## Files
-
-- `paper.pdf` — full pre-print (~6,000 words, 9 sections + 5 appendices)
-- `paper.html` — self-contained HTML version with embedded figures
-- `paper.md` — source markdown
-- `figures/` — 4 figures (PNG + SVG)
-- `REPRODUCIBILITY.md` — open methodology, how to reproduce in 90 minutes
-
-## Reproducibility
-
-The full methodology and calibration corpus description are documented in
-`REPRODUCIBILITY.md`, which is sufficient for independent re-implementation
-of the ensemble.
-
-A public mirror with the evaluation scripts (`eval_ensemble_corpus.py`,
-8 pinned regression tests, atomic-swap deploy with 30-second rollback)
-will be released within ~2 weeks following the v1.12 RU recalibration
-chain. Target reproduction infrastructure: Hetzner CX43 (8 vCPU, no GPU,
-~€14/month) or equivalent.
-
-For early access before the public mirror, please open a discussion on
-this dataset.
+All files (paper.pdf, paper.html, paper.md, REPRODUCIBILITY.md, figures/) are
+identical to the canonical dataset. See the canonical README for details, code
+repository roadmap, and citation.
 
 ## Cite as
 
 ```bibtex
 @misc{contentos2026,
   title={ContentOS: A Reproducible Bilingual AI-Text-Detection Ensemble with Adversarial Robustness Evaluation},
-  author={Humanswith.ai team},
+  author={Shevchenko, Gregory and Humanswith.ai team},
   year={2026},
-  url={https://huggingface.co/datasets/gshevchenko/contentos-preprint},
+  url={https://huggingface.co/datasets/Humanswith-ai/contentos-preprint},
 }
 ```
-
-## License
-
-MIT for code, methodology, and corpus aggregation. Underlying data sources retain their original licenses (HC3, AINL-Eval-2025, ai-text-detection-pile).