Topic
Quality & Prediction
The largest class, and the one that runs before ranking rather than during it: a set of models that predict how a human rater would score a page or a site. Most of what people mean by “E-E-A-T signals” lives here — and so does the calibration layer that decides what the predictions are trained against.
01Systems8
- 06BQ* (Balance)Q* is a summary quality score for the entire website — a 'balance' made up of the site's authority (siteAuthority), a low-quality flag (lowQuality), and the overall NSR value. This score changes slowly: internal Google documents describe it as 'largely static' — a sluggish foundation, not a day-to-day signal.
- 07B/CNSR (+ Fallback inheritance)NSR (Normalized Site Rank) is the real quality core — a normalized quality and authority value computed for individual 'chunks' (subsections) of a website. New sections initially inherit the average of their neighboring chunks ('fallback inheritance'). The historicized signal (predictedDefaultNsr) shows development over time — the trajectory matters, not today's single value.
- 08Bchard (+ YMYL/Hoax)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.
- 09BcontentEffortA 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.
- 10B/Ctofu / keto / Rhubarb / SubchunksThese codenames stand for refinement signals that fine-tune the quality estimate at a very granular level — individual subsections (subchunks) of a page. Their exact function is not fully documented in the available material. They are not a standalone main system but refine chard and NSR at the section level.
- 11B/COriginalContentScoreThis score measures how much first-hand material a piece of content contains — information found only there, that an AI could not reconstruct from other sources. The higher the score, the more original the content. It is the closest machine equivalent to the E-E-A-T dimension 'Experience' (lived experience and direct observation).
- 12AIS-Calibration (Rater/QRG)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.
- 13BTopic-Embeddings (Focus/Radius)Google computes a kind of 'topic map' from the entire text content of a website (embedding — a mathematical vector representation of the topic space). siteFocusScore measures how sharply the site's topic field is defined; siteRadius measures how widely it scatters thematically. A small radius means: a tightly focused site. The system measures thematic consistency, not subject-matter competence.
02Deep Dives9
- Q* and P*: The Two Pillars of Google's Ranking ArchitectureThe 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 ModelsThe 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 ModelThe 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 OriginalityThe 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 EffortThe 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 AboutThe focus vectors and radius that determine topical relevance — and why a weather site needs a narrow focus to rank for weather queries
- tofu, keto, Rhubarb: The Subchunk Refinement SignalsThe 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
- Site Reputation Abuse: What Google Suspended in Europe — and What It Did NotWhat Google suspended in the EEA is the manual action — the replacement removes exactly what parasite SEO pays for · also in Penalty & Spam
- What Good Content Is: contentEffort and Google's Soft MetricsOne leaked field, one rater definition, twelve official questions, and a checklist that marks which parts are evidence and which are our reading.
03Leak fields14
- B
siteAuthorityAuthority distilled from quality_nsr, applied in Q*.Q* / NSR - B
lowQualityLow-quality flag, converted from quality_nsr.NsrData.Q* - B
predictedDefaultNsrHistoricized baseline quality score (VersionedFloatSignal → the trajectory counts).NSR - B
nsrConfidenceConfidence in its own NSR judgment (deprecated).NSR - B
chardScoreContent-quality value per document, calibrated to rater judgments.chard - B/C
chardVarianceDispersion/uncertainty of the chardScore (high = an uncertain judgment).chard - B
contentEffortLLM-estimated creation effort and depth of a page.contentEffort - B/C
OriginalContentScoreOriginality/first-hand-material degree — the closest machine Experience proxy.OCS - B/C
tofu / keto / RhubarbCodenames for refinement/delta signals at the subchunk level.tofu-Familie - B
siteEmbeddingsVector representation of the site's topic space.Topic-Embeddings - B
siteFocusScoreHow sharply outlined the site's topic field is.Topic-Embeddings - B
siteRadiusHow widely the site scatters in topic space (small = focused).Topic-Embeddings - A
IS-ScoreInformation Satisfaction from rater judgments — the calibration norm.IS-Kalibrierung - B/C
siteQualityStddevStandard deviation of quality across a site (consistency).IS-Kalibrierung