An AI tool measuring AI’s footprint in science. No detector, no black box — just counts of documented LLM-tell words in PubMed. The collective jump after ChatGPT is the fingerprint; no single paper is accused. The self-reference is part of the point: the instrument discloses that it is itself built with AI.
PubMed via NCBI E-utilities (Public domain (NLM/NCBI); counts only). Only hit counts per query/year — no full texts, no persons.
On every build. PubMed indexes with a lag — the current year is excluded, recent years are incomplete (which is why the peak sits in 2024, not later). Canonical artefact: versioned JSON in src/data/tell/ — git is the archive.
Deterministic: per marker and year, hits in title/abstract (esearch), normalised per 100,000 abstracts. The index is the sum of marker shares. Baseline = pre-ChatGPT mean; peak = highest-index year; fold = peak / baseline.
v1 only counts (no model). Planned v2: a transparent LLM classifier estimating a synthetic-likelihood per abstract, verified against the marker counts — prompt disclosed, uncertainty as part of the measurement. Condition: never an unaccountable oracle.
About 64 keyless HTTP count requests per build, no LLM. The site is static.