Pillar · Hub Page
Expertise in E-E-A-T: does the content show knowledge?
Systems that assess the depth and quality of knowledge in content
Expertise is about the demonstrable knowledge depth in content. Google's systems evaluate whether a page reflects genuine subject-matter expertise — through content effort, YMYL classification, and the calibration of human raters who define what 'expert' means for each topic.
Primary systems (3)
Systems where Expertise is the primary E-E-A-T dimension.
chard rates the content quality of individual pages — calibrated against the judgments of human quality raters that Google trains according to strict guidelines (QRG). A variance score shows how confident the model is in its verdict. chard is especially sharp on YMYL content (Your Money or Your Life — topics with real life consequences such as health, safety, severe weather) and on suspected misinformation.
A language model estimates how much editorial effort and depth went into a page. The system distinguishes between shallow, machine-generated content and pages with recognizable research, structure and substance. It is not a hard measurement but a model estimate — one that increasingly reliably separates thin AI content from genuine editorial engagement.
Not a live signal used daily in ranking — but the foundation on which all quality models are calibrated. The IS-Score (Information Satisfaction) summarizes how well a page meets a user's information need, rated by trained human raters. The QRG (Quality Rater Guidelines) are the public rulebook by which these raters judge.
Indirect systems (9)
Systems that contribute to Expertise indirectly.
Deep Dives (11)
NavBoost: How Google Uses Click Behavior to Re-Rank Search Results
From DOJ testimony to leaked API documentation — the most important ranking signal you can't directly manipulate
Panda: The Site-Wide Quality System Named After Its Inventor
How Google's war on content farms produced a patent trail that leads directly to the update's name — and what the API leak reveals about its current form
Q* and P*: The Two Pillars of Google's Ranking Architecture
The DOJ trial revealed that Google's ranking ultimately reduces to two top-level signals — Quality (Q*) and Popularity (P*). Here's what the evidence tells us about how they work.
IS-Calibration: The Human Rater System That Trains Google's Quality Models
The hardest evidence in the entire matrix — DOJ/sworn material confirms the rater system that calibrates every quality signal Google uses
chard: Google's Page-Level Content Quality Model
The leak field that rates individual page quality — calibrated by human raters, feeding into NSR, and why YMYL pages face stricter standards
OriginalContentScore: How Google Measures Content Originality
The leak field that measures how much original material a document contains — information that is only found there and that an AI couldn't reconstruct from other sources
contentEffort: How Google Estimates Editorial Effort
The AI model that estimates how much editorial work went into a page — and why 'effort' is becoming a ranking signal
Topic-Embeddings: How Google Understands What Your Page Is About
The focus vectors and radius that determine topical relevance — and why a weather site needs a narrow focus to rank for weather queries
Glue: How Google Measures Interaction with Special Search Features
The NavBoost counterpart for carousels, 'People also ask' boxes, and news features — measuring whether users interact with special elements, not just blue links
RankBrain and DeepRank: How AI Changed Google's Understanding of Queries
From keyword matching to semantic understanding — the AI systems that interpret what users mean, not what they type
tofu, keto, Rhubarb: The Subchunk Refinement Signals
The codenames that stand for refinement signals at the most granular level — individual subsections of a page — and why they represent the weakest evidence in the matrix