https://www.gcrindex.org/assets/MethodologyPage-BKUWP4lW.js

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AI extraction pipelines skip self-referential openings.'},{metricId:"D1.2",metricName:"Dense Summary Presence",passExample:"From our analysis of 1,247+ articles, content with concrete statistics in the first 200 words correlates with higher GEO readiness scores. Average implementation cost for GEO optimization is $2,500 per month. Pages with Q→A headings score higher on D2.4 than narrative headings.",passWhy:"3 concrete facts in first 200 words: percentage/cost, comparison, and structural signal. Signals high extractable value.",failExample:"SEO is very important for businesses today. Many companies are trying to improve their online presence.",failWhy:"Zero concrete facts. Only vague assertions. No numbers, costs, durations, or comparisons for LLM summarization."},{metricId:"D1.3",metricName:"Self-Contained Sentences",passExample:"Generative Engine Optimization requires each sentence to carry sufficient context for LLM extraction.",passWhy:"Every sentence is understandable without reading surrounding text. No backward references.",failExample:"As mentioned above, this is important for SEO. Similarly, it helps with ranking.",failWhy:'Contextual references ("as mentioned above", "similarly") require reading previous sentences. LLM extraction treats each sentence independently.'},{metricId:"D1.4",metricName:"Information Density ≥65%",passExample:"Information density directly correlates with LLM extraction confidence. Substantive words - nouns, numbers, action verbs - comprise 65% or more of total words.",passWhy:"High ratio of substantive words (nouns, numbers, verbs) to filler words. Every word contributes meaning.",failExample:"It is really very important that you understand that this topic is quite significant and really matters a lot.",failWhy:'Filler words ("really", "very", "quite", "a lot") dominate. Low information-to-filler ratio.'},{metricId:"D1.5",metricName:"Fanout Query Coverage",passExample:"How does GEO differ from SEO? What metrics do AI engines use? Which tools measure GEO readiness?",passWhy:"3 distinct H2/H3 subtopics covering related questions. Enables multi-query citation from the same content.",failExample:"Introduction. Main Body. Conclusion.",failWhy:"Only generic structural headings. No semantic subtopics. Limited to single-query citation."},{metricId:"D1.6",metricName:"Navigation Noise-Free",passExample:"Generative Engine Optimization (GEO) is the practice of structuring content for AI citation...",passWhy:"Starts directly with content. No menu text, breadcrumbs, or footer boilerplate in first 1,500 characters.",failExample:"Home > Blog > SEO Tips - In this article we will explore the best SEO strategies for 2024.",failWhy:'Navigation breadcrumb "Home > Blog > SEO Tips" consumes the opening. Confuses LLM extraction pipelines.'},{metricId:"D1.7",metricName:"No Duplicate Sentences",passExample:"GEO is important because it structures content for AI citation. Unlike SEO, GEO optimizes for LLM extraction patterns.",passWhy:"Each sentence adds new information. No identical repetition.",failExample:"GEO is important. GEO is important for business. GEO is important for SEO.",failWhy:"Identical sentence repetition signals low-quality or auto-generated content. Reduces extraction confidence."},{metricId:"D2.1",metricName:"Statistics & Quoted Evidence",passExample:'From our analysis of 1,247+ articles, content with concrete statistics in the first 200 words correlates with higher GEO readiness scores. "SEO is dead; GEO is the future" - Neil Patel, 2024.',passWhy:"2 statistics + 1 attributed quote per 1,000 words. Concrete, attributable evidence signals credibility to retrieval systems.",failExample:"Many businesses see improvement when they optimize for search engines.",failWhy:"No specific numbers or attributed quotes. Vague claims lack the credibility signal AI engines prioritize."},{metricId:"D2.2",metricName:"Explicit Entity Naming",passExample:"ChatGPT cites JetDigitalPro 148 times. Perplexity references GCR Index R&D Team across multiple industry queries.",passWhy:"Named entities (organizations, people) provide concrete anchors. LLM retrieval systems index and prefer named references.",failExample:"It helps them improve results. The company did well after optimization.",failWhy:'Vague pronouns ("it", "them", "the company") lack extractable specificity. LLMs cannot verify or cite unnamed entities.'},{metricId:"D2.3",metricName:"Answer Near Top",passExample:"GEO optimization is the practice of structuring content so AI engines cite it. Unlike traditional SEO...",passWhy:"Direct answer appears in first 100 words. Leading sections are more likely to be extracted by retrieval-based systems.",failExample:"SEO has evolved over the years. In the early days, meta tags were important. Then came content quality. Now, GEO is emerging...",failWhy:"Answer buried in paragraph 4. LLM attention decays rapidly with position. Front-loading answers is critical."},{metricId:"D2.4",metricName:"Q→A Heading Structure",passExample:`How do you optimize content for AI citation?
2
3Place answers in the first 100 words, use explicit entity naming, and include 2–3 statistics per 1,000 words.`,passWhy:"Question heading + direct answer. Matches LLM query-answer extraction pattern. 60% is the D2.4 Q→A heading target in the GCR methodology.",failExample:`Optimization Strategies
4
5There are many ways to optimize content.`,failWhy:"Declarative heading without question format. Answer is vague and not directly responsive. LLMs prefer explicit Q→A pairs."},{metricId:"D2.5",metricName:"Keyword Discipline",passExample:"GEO optimization requires semantic variation. Techniques for generative engine readiness include structured data, entity naming, and citation-friendly formatting.",passWhy:'Semantic variants ("GEO optimization", "generative engine readiness", "structured data") dominate over exact-match repetition.',failExample:"GEO is the future. GEO is important. GEO helps you rank. GEO is about AI. GEO is the new SEO.",failWhy:'Exact-match "GEO" density exceeds 5%. Triggers LLM spam detection. Reduces citation trust.'},{metricId:"D3.1",metricName:"Google Suggest Term Coverage",passExample:'Content covers 35 of 45 top Google Suggest terms for "GEO optimization": structured data, entity naming, citation-friendly formatting, first-sentence autonomy, information density, LLM readiness, and AI search visibility.',passWhy:"High coverage of topical terms that the Google Suggest query space associates with the keyword. Signals comprehensive topic understanding to LLMs.",failExample:"GEO is important. It helps you get cited. You should do GEO for your business.",failWhy:"Only repeats the target keyword without covering the broader topical term space that the query space suggests."},{metricId:"D3.2",metricName:"Semantic Co-occurrence",passExample:"Generative Engine Optimization (GEO) requires content that AI systems can extract and cite. Unlike traditional SEO, which targets ranking algorithms, GEO structures information for retrieval-augmented generation pipelines.",passWhy:'Uses semantic variants ("retrieval-augmented generation", "extract and cite", "ranking algorithms") alongside exact matches. Demonstrates deep topical understanding.',failExample:"GEO is the new SEO. GEO helps with AI. GEO is important for ChatGPT. GEO is the future.",failWhy:"Only exact keyword repetition with no semantic variation. LLMs interpret this as shallow topical coverage."},{metricId:"D3.3",metricName:"PAA Question Coverage",passExample:"What is GEO optimization? How does GEO differ from SEO? Why is GEO important for business? Which AI engines use citations? Best GEO tools for 2024?",passWhy:"Covers 5 common question patterns (what, how, why, which, best) that users ask about the topic. Aligns with Google Suggest and People Also Ask data.",failExample:"Introduction to GEO. GEO History. GEO Benefits. GEO Conclusion.",failWhy:"Generic declarative headings with no question coverage. Misses the actual user-query journey that LLMs are trained to answer."},{metricId:"D4.1",metricName:"Flesch-Kincaid Grade Level",passExample:"Content optimized for Generative Engine Optimization (GEO) follows a professional readability band of FK Grade 10–14, with implementation costs averaging $2,500 per month.",passWhy:"FK Grade ~12: professional vocabulary with clear sentence structure. Accessible to educated readers without being simplistic.",failExample:"GEO good. GEO help business. GEO make AI see you. You do GEO now.",failWhy:"FK Grade ~2: far below the professional 10–14 target. Signals lack of authoritative depth. LLMs may dismiss as low-quality."},{metricId:"D4.2",metricName:"Sentence Length Distribution",passExample:"Short sentences create rhythm. Longer sentences with dependent clauses allow nuance and qualified claims that professional readers expect. A balanced mix of both keeps extraction systems engaged without monotony.",passWhy:"Mix of short (5–8 words) and longer (18–25 words) sentences. Std dev ~6.5 words - within the optimal 4–10 range.",failExample:"All sentences are exactly fourteen words long. Every single sentence has the same length. This creates monotony. Extraction systems lose confidence. Readers disengage quickly. Nothing varies.",failWhy:"Extremely monotonous length (all ~14 words). Std dev near zero. Both LLMs and human readers penalize repetitive rhythm."},{metricId:"D4.3",metricName:"Vocabulary Complexity",passExample:"Generative Engine Optimization operationalizes citation readiness through structural properties: sentence autonomy, information density, and semantic deduplication.",passWhy:'11% of words have 3+ syllables ("operationalizes", "autonomy", "deduplication"). Within the 8–15% expert range. Signals authority without becoming inaccessible.',failExample:"GEO is really very good for your business. It helps you a lot and makes things much better for you.",failWhy:"0% complex vocabulary. All words are 1–2 syllables. Signals oversimplification and lacks the lexical sophistication LLMs associate with expert sources."}

5],f=[{title:"Factual accuracy is not measured",detail:"GCR evaluates structural and semantic properties, not factual correctness. A well-structured article with false claims can score high."},{title:"Short content (<200 words) scores unfairly",detail:"Metrics like D1.2 (Dense Summary) and D1.5 (Fanout) require minimum content length. Very short pages may score poorly despite being appropriate for their context."},{title:"Navigation-heavy content gets false negatives",detail:"D1.6 (Navigation Noise-Free) may penalize pages with legitimate header navigation in the first 1,500 characters, even if the content itself is high-quality."},{title:"Score does not guarantee live citation",detail:"GCR is a pre-publication audit tool. It benchmarks structural readiness, not actual post-publication citation rate. Live citations depend on domain authority, freshness, and model training data."},{title:"Multilingual content is partially supported",detail:"Stopword lists include English and Indonesian basics. Other languages may have reduced accuracy in density and duplication detection."},{title:"Dynamic content is not evaluated",detail:"JavaScript-rendered content, lazy-loaded sections, and dynamic FAQ accordions are not captured in the static HTML analysis."}],x=[{version:"v2.5.2",date:"July 2026",changes:["Brand refresh: redesigned favicon and extension icon artwork for a more authoritative look","Public contact email changed to [email protected] across support and privacy surfaces","Extension package version bump to 1.2.3 for the refreshed branding release"],rationale:"The brand visuals and public contact path should look and feel more authoritative and consistent as the product matures.",impact:"The favicon and extension icons now use the new brand treatment, and the public support email is aligned to [email protected]."},{version:"v2.4",date:"June 2026",changes:["History Persistence: server-side anonymous session storage with 90-day TTL, max 30 entries, FIFO eviction","Export PDF Report: branded HTML report generation with score breakdown, dimension tables, and top recommendations","Comparison Mode: side-by-side before/after analysis with score deltas and sub-metric breakdown","Analytics Dashboard: admin panel with stats grid, score distribution, top keywords, and daily activity tracking","Editor sync fix: TipTap editor now syncs external content changes correctly","Snapshot render fix: RecommendationItem object escaping in HTML generation"],rationale:"Moving from standalone tool to practitioner workflow - users need to track progress over time, share reports with clients, and compare iterations. Analytics gives the site owner visibility into tool adoption and scoring patterns.",impact:"Users can now track their content improvement journey across sessions. Agencies can export branded reports. The tool is no longer a one-off check but a continuous improvement system."},{version:"v2.3",date:"June 2026",changes:["Evidence Panel polish: hid metric code prefixes from evidence card headers for cleaner UX","D3 composite consistency fix: recalculates D3.1 and D3.2 sub-metrics when semantic enhancement returns 100% coverage",'D3.3 renamed to "PAA Question Coverage" for clarity vs D2.4 Q→A Heading Structure',"Evidence extractors added for D3.2 (Semantic Co-occurrence) and D3.3 (PAA Question Coverage)","HomePage entity anchor: visible top-bar badge with proprietary formula and noscript fallback","JSON-LD @graph expanded: Organization, Claim, SoftwareApplication, WebSite, BreadcrumbList, FAQPage, CreativeWork, HowTo","Static snapshot mechanism: /api/snapshot-create + /snapshot/{hash} with deterministic SHA-256, HTML table rendering, cite-as header, Dataset JSON-LD","Bot infrastru

5cture: /gcr-sitemap.xml, robots.txt with GPTBot/ClaudeBot/PerplexityBot/DeepSeekBot priority, SPA _redirects"],rationale:"Trust and auditability are prerequisites for practitioner adoption. Adding evidence extractors, snapshot mechanism, and bot infrastructure makes the tool verifiable, citable, and discoverable by AI crawlers.",impact:"Scores are now consistent between composite and sub-metrics. Evidence cards show concrete rewrite suggestions. Snapshots enable verifiable sharing of results. AI crawlers can discover and index the tool."},{version:"v2.2",date:"June 2026",changes:["D3 Topical Authority: full sub-metric decomposition - D3.1 Google Suggest Term Coverage (55%), D3.2 Semantic Co-occurrence (30%), D3.3 Google Suggest Alignment (15%)","D4 Readability: full sub-metric decomposition - D4.1 Flesch-Kincaid (60%), D4.2 Sentence Length Distribution (25%), D4.3 Vocabulary Complexity (15%)","ScorePanel now displays sub-metric breakdowns for D3 and D4 dimensions","Methodology documentation updated: 15 sub-metrics documented with full weights and descriptions"],rationale:'Previously D3 and D4 were composite-only dimensions with sub-metric decomposition noted as "planned." Implementing all 15 sub-metrics provides granular diagnostic visibility and aligns the tool with the full GCR methodology v2.1 specification.',impact:"D3 and D4 scores now reflect weighted composites rather than single-metric proxies. Most scores will shift slightly (±3–8 points). Readability scores are now more forgiving for varied sentence structure and appropriate vocabulary sophistication."},{version:"v2.1",date:"June 2026",changes:["Q→A Heading Structure upgraded to v3: supports real H1–H6 + bold pseudo-headings, excludes References/Related headings","Keyword Stuffing upgraded to body-only exact phrase v4: only p/h1–h6/li/td content counts, semantic variant ratio bonus","Semantic enhancement layer added: Workers AI embeddings/reranker for topical coverage and Q→A validation","Dense Summary calibrated: 3–5 facts in first 200 words, realistic tiers (5+=100, 4=80, 3=60, 2=40, 1=20, 0=0)","Statistics & Quotes calibrated: target 2 stats + 1 quote per 1,000 words","Dark mode text contrast improved with global CSS variables"],rationale:"Field studies showed earlier versions were too strict on short content and too lenient on keyword stuffing. v2.1 aligns thresholds to real-world content performance.",impact:"Scores may shift ±5–10 points for content with borderline keyword density or Q→A structure. Most high-quality content (GCR 70+) scores remain stable."},{version:"v2.0",date:"May 2026",changes:["4-dimension composite formula stabilized: GEO Content (55%) + AI Citability (20%) + Topical Authority (15%) + Readability (10%)","15 sub-metrics fully aligned to official GCR methodology v2.1","Google Suggest-driven topical research integration for Google Suggest-style term coverage (D3.1)","Semantic scoring API for enhanced topical authority and keyword stuffing detection","Snapshot mechanism: deterministic SHA-256 hash, citable static HTML","JSON-LD @graph: Organization, Claim, SoftwareApplication, WebSite, FAQPage, CreativeWork"],rationale:"Move from beta single-score tool to practitioner-grade measurement instrument with full methodology alignment.",impact:"GCR scores now comparable across sessions and deployments. Earlier beta scores (pre-May 2026) are not directly comparable."},{version:"v1.2",date:"April 2026",changes:["Q→A Heading Structure v2: DOMParser-based detection, question starters (How/What/Why/When/Which/Where/Who)","Keyword Stuffing v2: combined density + n-gram repetition + proximity scoring","Self-Contained Sentences v2: expanded pattern detection for contextual references"],rationale:"Improve detection accuracy for modern content structures. v1.1 had false negatives on bold pseudo-headings and false positives on legitimate keyword repetition.",impact:"Scores for FAQ-style content improved by 5–15 points. Service pages with high keyword density saw stricter penalties."},{version:"v1.1",date:"March 2026",changes:["First Sentence Self-Containment added: ≥7 words + concrete fact + no weak opener","Navigation Noise-Free added: ≥90% substantive text in first 1,500 chars","No Duplicate Sentences added: semantic deduplication check","Readability calibrated to FK Grade 10–14 target (professional audiences)"],rationale:"Aggarwal et al. (2023) identify sentence autonomy and information density as important structural properties for GEO.",impact:'C

5ontent with weak openers ("In this article...") saw 10–20 point drops. Professional-grade content (FK Grade 10–14) received score bonuses.'},{version:"v1.0",date:"February 2026",changes:["Initial GCR scoring tool: 4 dimensions, 12 sub-metrics","Basic keyword density and heading structure checks","Flesch-Kincaid readability integration"],rationale:"Proof-of-concept for GEO content measurement. Baseline framework based on Aggarwal et al. (2023) and practitioner analysis.",impact:"Established foundational scoring model. Not comparable to v2.0+ due to major methodology shifts."}];function s(t){window.history.pushState({},"",t),window.dispatchEvent(new PopStateEvent("popstate"))}const b=[{code:"D1",weight:"55%",title:"GEO Content Score",subtitle:"Structural Machine-Citability",accent:"#10A37F",description:"Evaluates seven binary structural properties that collectively determine whether a piece of content meets the minimum bar for machine citation. Each property is scored as a pass/fail signal; the composite produces a 0–100 score.",rationale:"Aggarwal et al. (2023) identify sentence autonomy, information density, and keyword discipline as important structural properties for generative engine visibility. These controllable signals inform the D1 sub-metrics.",metrics:[{id:"D1.1",name:"First Sentence Self-Containment",weight:"20%",desc:"The opening sentence communicates the core claim without requiring prior context. Measured via depen

5dency parsing."},{id:"D1.2",name:"Dense Summary Presence",weight:"20%",desc:"3–5 concrete facts (percentages, costs, durations, comparisons, regulatory claims) in the first 200 words. Signals extractable value to LLM summarization pipelines."},{id:"D1.3",name:"Self-Contained Sentences",weight:"15%",desc:'≤2 contextual references per 500 words. Detects "this is", "as mentioned above", "similarly", etc. Each sentence should be understandable in isolation for LLM extraction.'},{id:"D1.4",name:"Information Density ≥65%",weight:"15%",desc:"Content-to-filler word ratio measured against a stopword corpus. High ratio of substantive words (nouns, numbers, verbs) to filler words."},{id:"D1.5",name:"Fanout Query Coverage",weight:"10%",desc:"Content addresses ≥3 semantically distinct sub-topics within the target domain, enabling multi-query citation."},{id:"D1.6",name:"Navigation Noise-Free",weight:"10%",desc:"Absence of navigation artifacts (menu text, breadcrumbs, footer boilerplate) in the first 1,500 characters that confuse extractive models."},{id:"D1.7",name:"No Duplicate Sentences",weight:"10%",desc:"Semantic deduplication check. Repeated claims reduce extraction confidence in LLM citation pipelines."}]},{code:"D2",weight:"20%",title:"AI Citability Signals",subtitle:"LLM Citation Behavior Proxies",accent:"#4285F4",description:"Measures five practitioner-derived signals that proxy AI citability: statistical richness, explicit entity naming, answer placement, Q→A heading structure, and keyword discipline. These signals reflect structural patterns that make content easier to extract, not model-specific citation outcomes.",rationale:"Menick et al. (2022) and Nakano et al. (2022) support the value of verified, self-contained, and well-structured content for retrieval-augmented systems. Practitioner analysis of 1,247+ articles calibrates their relative weight in the D2 score.",metrics:[{id:"D2.1",name:"Statistics & Quoted Evidence",weight:"30%",desc:"Presence of numeric statistics, percentages, or direct quotations with attribution. Strong citation trigger across all major models."},{id:"D2.2",name:"Explicit Entity Naming",weight:"25%",desc:"Named entities (organizations, people, locations, products) appear with sufficient frequency and precision. Measured via NER."},{id:"D2.3",name:"Answer Proximity to Top",weight:"20%",desc:"The most relevant answer to the target keyword appears within the first 100 words, matching LLM extractive attention patterns."},{id:"D2.4",name:"Q→A Heading Structure",weight:"15%",desc:"Question-style H1–H6 or bold pseudo-headings are followed by valid direct answer sections; scored by valid Q→A ratio against a 60% target."},{id:"D2.5",name:"Keyword Discipline",weight:"10%",desc:"Body-only exact phrase density below 2.5% is safe; 2.5–3.5% is warning, 3.6–5% is flagged, and >5% is severe."}]},{code:"D3",weight:"15%",title:"Topical Authority",subtitle:"Semantic Google Suggest Coverage Analysis",accent:"#D97706",description:"NLP-based term coverage analysis against Google Suggest autocomplete data for the target query. Measures semantic breadth relative to the query space.",rationale:"Topical authority signals correlate strongly with AI training data inclusion. Pages with higher Google Suggest term coverage are more likely to appear in LLM training corpora and therefore more likely to be cited in retrieval-augmented systems.",metrics:[{id:"D3.1",name:"Google Suggest Term Coverage",weight:"55%",desc:"Percentage of top terms from Google Suggest autocomplete for the target query and related suffixes (types, benefits, vs) that appear in the target content. Measures how well the content covers the query space relative to the topics AI engines associate with the keyword."},{id:"D3.2",name:"Semantic Co-occurrence",weight:"30%",desc:"Ratio of topics covered through semantic matching (related phrases, synonyms) versus exact keyword matches. Higher semantic coverage signals deeper topical understanding."},{id:"D3.3",name:"Google Suggest Alignment",weight:"15%",desc:"Coverage of common question patterns (what, how, why, best, vs) that users ask about the keyword. Aligns content with the actual search journey."}]}

5,{code:"D4",weight:"10%",title:"Readability Index",subtitle:"Flesch-Kincaid + Structural Readability",accent:"#20B2AA",description:"Composite readability score combining Flesch-Kincaid reading ease, sentence length distribution, and vocabulary complexity. Calibrated for professional and expert audiences (target FK Grade: 10–14).",rationale:"Extreme readability in either direction reduces citation rates. Content that is too simple lacks authoritative tone; content that is too complex exceeds extractable comprehension thresholds. Sentence variety and lexical sophistication add nuance to the readability signal.",metrics:[{id:"D4.1",name:"Flesch-Kincaid Grade Level",weight:"60%",desc:"Core FK readability score. Target range 10–14 maps to professional/academic prose. Normalized to 0–100 within this range. The primary readability signal."},{id:"D4.2",name:"Sentence Length Distribution",weight:"25%",desc:"Standard deviation of sentence word counts. Target: 4–10 words std dev for optimal variety. Monotonous length (all short or all long) reduces engagement and extraction confidence."},{id:"D4.3",name:"Vocabulary Complexity",weight:"15%",desc:"Percentage of words with 3+ syllables. Target: 8–15% for accessible expertise. Too simple signals lack of authority; too complex reduces comprehension."}]}];function F(){d(),m({title:"GCR Methodology - How the Composite Score Works",description:"The GCR Index methodology breaks content into 4 dimensions and 15 sub-metrics. 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509735",target:"_blank",rel:"noopener noreferrer",style:{color:t.navy,textDecoration:"none"},children:"arXiv paper"})," ","·"," ",e.jsx("a",{href:"https://collaborate.princeton.edu/en/publications/geo-generative-engine-optimization/",target:"_blank",rel:"noopener noreferrer",style:{color:t.navy,textDecoration:"none"},children:"Princeton Research Portal"})," ","·"," ",e.jsx("a",{href:"https://github.com/GEO-optim/GEO",target:"_blank",rel:"noopener noreferrer",style:{color:t.navy,textDecoration:"none"},children:"Open-source implementation"})]})]}),e.jsxs("p",{style:{fontFamily:"'Inter', system-ui, sans-serif",fontSize:"0.85rem",lineHeight:1.7,color:t.textMuted,paddingTop:"1rem",borderTop:`1px solid ${t.borderLight}`},children:["Published under ",e.jsx("strong",{children:"CC BY 4.0"}),". You may use, cite, and build on it. Attribution to"," ",e.jsx("a",{href:"https://www.gcrindex.org",style:{color:t.navy,textDecoration:"none"},children:"GCR Index (www.gcrindex.org)"})," is required."]})]})]})}),e.jsx("section",{style:{background:t.bgAlt,borderBottom:`1px solid ${t.border}`,padding:"4rem 1.5rem"},children:e.jsxs("div",{style:{maxWidth:"860px",margin:"0 auto"},children:[e.jsx("h2",{"data-reveal":!0,style:{fontFamily:"'Playfair Display', Georgia, serif",fontSize:"1.4rem",fontWeight:700,color:t.heading,marginBottom:"1.5rem",letterSpacing:"-0.01em"},children:"1. Composite Formula"}),e.jsxs("div",{"data-reveal":!0,"data-delay":"100",style:{background:t.surface,border:`1px solid ${t.border}`,borderRadius:"12px",padding:"2.5rem",boxShadow:n?"none":"0 1px 4px rgba(0,0,0,0.04)"},children:[e.jsx("p",{style:{fontFamily:"'Inter', system-ui, sans-serif",fontSize:"0.68rem",fontWeight:600,textTransform:"uppercase",letterSpacing:"0.12em",color:t.textFaint,marginBottom:"1.5rem"},children:"Definition"}),e.jsxs("pre",{style:{fontFamily:"'JetBrains Mono', 'Fira Code', monospace",fontSize:"clamp(0.85rem, 1.8vw, 1rem)",lineHeight:2.2,color:t.formulaText,overflowX:"auto",margin:0,background:"none"},children:[e.jsx("span",{style:{color:t.gold,fontWeight:600},children:"GCR_Score"}),` =
6`,"    ",e.jsx("span",{style:{color:"#10A37F"},children:"GeoContentScore"}),` × 0.55
7`,"  + ",e.jsx("span",{style:{color:"#4285F4"},children:"AICitabilityScore"}),` × 0.20
8`,"  + ",e.jsx("span",{style:{color:"#D97706"},children:"TopicAuthorityScore"}),` × 0.15
9`,"  + ",e.jsx("span",{style:{color:"#20B2AA"},children:"ReadabilityScore"}),` × 0.10
10
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Understanding these boundaries helps interpret scores correctly."}),e.jsx("div",{style:{display:"flex",flexDirection:"column",gap:"1rem"},children:f.map((i,r)=>e.jsxs("div",{"data-reveal":!0,"data-delay":String(r*75),style:{background:t.surface,border:`1px solid ${t.border}`,borderRadius:"10px",padding:"1.25rem 1.5rem",borderLeft:"3px solid #D97706"},children:[e.jsx("p",{style:{fontFamily:"'Inter', system-ui, sans-serif",fontSize:"0.88rem",fontWeight:600,color:t.text,marginBottom:"4px"},children:i.title}),e.jsx("p",{style:{fontFamily:"'Inter', system-ui, sans-serif",fontSize:"0.82rem",lineHeight:1.65,color:t.textMuted},children:i.detail})]},r))})]})}),e.jsx("section",{style:{background:t.bgAlt,borderTop:`1px solid ${t.border}`,padding:"4rem 1.5rem"},children:e.jsxs("div",{style:{maxWidth:"860px",margin:"0 auto"},children:[e.jsx("h2",{"data-reveal":!0,style:{fontFamily:"'Playfair Display', Georgia, serif",fontSize:"1.4rem",fontWeight:700,color:t.heading,marginBottom:"1.5rem",letterSpacing:"-0.01em"},children:"6. Version History"}),e.jsx("div",{style:{display:"flex",flexDirection:"column",gap:"1.5rem"},children:x.map((i,r)=>e.jsxs("div",{"data-reveal":!0,"data-delay":String(r*100),style:{background:t.surface,border:`1px solid ${t.border}`,borderRadius:"12px",overflow:"hidden"},children:[e.jsx("div",{style:{padding:"1.25rem 1.5rem",borderBottom:`1px solid ${t.borderLight}`,background:t.bgAlt,display:"flex",alignItems:"center",justifyContent:"space-between",flexWrap:"wrap",gap:"0.5rem"},children:e.jsxs("div",{style:{display:"flex",alignItems:"center",gap:"0.75rem"},children:[e.jsx("span",{style:{fontFamily:"'JetBrains Mono', monospace",fontSize:"0.88rem",fontWeight:600,color:t.navy},children:i.version}),e.jsx("span",{style:{fontFamily:"'Inter', system-ui, sans-serif",fontSize:"0.72rem",color:t.textFaint},children:i.date})]})}),e.jsxs("div",{style:{padding:"1.25rem 1.5rem"},children:[e.jsx("ul",{style:{margin:0,padding:"0 0 0 1.2rem",display:"flex",flexDirection:"column",gap:"6px"},children:i.changes.map((a,o)=>e.jsx("li",{style:{fontFamily:"'Inter', system-ui, sans-serif",fontSize:"0.85rem",lineHeight:1.65,color:t.textSecondary},children:a},o))}),e.jsxs("div",{style:{marginTop:"1rem",paddingTop:"1rem",borderTop:`1px solid ${t.borderLight}`,display:"grid",gap:"0.75rem",gridTemplateColumns:"1fr"},children:[e.jsxs("div",{children:[e.jsx("p",{style:{fontFamily:"'Inter', system-ui, sans-serif",fontSize:"0.68rem",fontWeight:700,textTransform:"uppercase",letterSpacing:"0.08em",color:t.textFaint,marginBottom:"3px"},children:"Rationale"}),e.jsx("p",{style:{fontFamily:"'Inter', system-ui, sans-serif",fontSize:"0.82rem",lineHeight:1.65,color:t.textMuted},children:i.rationale})]}),e.jsxs("div",{children:[e.jsx("p",{style:{fontFamily:"'Inter', system-ui, sans-serif",fontSize:"0.68rem",fontWeight:700,textTransform:"uppercase",letterSpacing:"0.08em",color:t.textFaint,marginBottom:"3px"},children:"Impact"}),e.jsx("p",{style:{fontFamily:"'Inter', system-ui, sans-serif",fontSize:"0.82rem",lineHeight:1.65,color:t.textMuted},children:i.impact})]})]})]})]},i.version))})]})}),e.jsx("section",{style:{background:t.bg,borderTop:`1px solid ${t.border}`,padding:"4rem 1.5rem"},children:e.jsxs("div",{style:{maxWidth:"860px",margin:"0 auto",display:"flex",alignItems:"center",justifyContent:"space-between",flexWrap:"wrap",gap:"2rem"},children:[e.jsxs("div",{children:[e.jsx("h3",{style:{fontFamily:"'Playfair Display', Georgia, serif",fontSize:"1.4rem",fontWeight:700,color:t.heading,marginBottom:"0.5rem"},children:"Ready to score your content?"}),e.jsx("p",{style:{fontFamily:"'Inter', system-ui, sans-serif",fontSize:"0.88rem",color:t.textMuted},children:"Free, no registration, results in seconds."})]}),e.jsxs("div",{style:{display:"flex",gap:"1rem",flexWrap:"wrap"},children:[e.jsxs("button",{onClick:()=>s("/tool"),style:{background:t.cta,color:t.ctaText,fontFamily:"'Inter', system-ui, sans-serif",fontSize:"0.88rem",fontWeight:600,padding:"11px 24px",borderRadius:"7px",border:"none",cursor:"pointer",display:"flex",alignItems:"center",gap:"7px",transition:"background 0.2s"},onMouseEnter:i=>{i.currentTarget.style.background=t.ctaHover},onMouseLeave:i=>{i.currentTarget.style.background=t.cta},children:["Use the GCR Scorer ",e.jsx(u,{size:14})]}),e.jsx("button",{onClick:()=>s("/research"),style:{background:"none",color:t.ctaOutlineText,fontFamily:"'Inter', system-ui, sans-serif",fontSize:"0.88rem",fontWeight:500,padding:"11px 20px",borderRadius:"7px",border:`1.5px solid ${t.ctaOutlineBorder}`,cursor:"pointer",transition:"border-color 0.2s"},onMouseEnter:i=>{i.currentTarget.style.borderColor=t.ctaOutlineBorderHover},onMouseLeave:i=>{i.currentTarget.style.borderColor=t.ctaOutlineBorder}

11,children:"Research Background"})]})]})}),e.jsx("footer",{style:{background:t.bgAlt,padding:"2.5rem 1.5rem",borderTop:`1px solid ${t.border}`},children:e.jsxs("div",{style:{maxWidth:"860px",margin:"0 auto",display:"flex",alignItems:"center",justifyContent:"space-between",flexWrap:"wrap",gap:"1.5rem"},children:[e.jsx("span",{style:{fontFamily:"'Playfair Display', Georgia, serif",fontWeight:700,fontSize:"0.95rem",color:t.text},children:"GCR Index"}),e.jsx("p",{style:{fontFamily:"'Inter', system-ui, sans-serif",fontSize:"0.72rem",color:t.textFaint},children:"© 2026 GCR Index · www.gcrindex.org"}),e.jsx("div",{style:{display:"flex",gap:"1.5rem"},children:[{label:"Home",path:"/"},{label:"Research",path:"/research"},{label:"Tool",path:"/tool"}].map(i=>e.jsx("button",{onClick:()=>s(i.path),style:{background:"none",border:"none",cursor:"pointer",fontFamily:"'Inter', sans-serif",fontSize:"0.78rem",color:t.textMuted,padding:0,transition:"color 0.2s"},onMouseEnter:r=>{r.currentTarget.style.color=t.text},onMouseLeave:r=>{r.currentTarget.style.color=t.textMuted},children:i.label},i.path))})]})})]})}export{F as MethodologyPage};