A reusable diagnostic protocol for distinguishing general web indexing from inclusion in AI summarizer retrieval and injection layers. Its seven-phase workflow tests technical indexing, semantic parsing, entity recognition, summarizer appearance, cross-platform confirmation, delta behavior, and recurring visibility patterns. The protocol organizes queries into direct-reference, conceptual, adjacent, and control tiers; records exact wording, dates, sources, citations, screenshots, and output differences; and classifies results such as fully discoverable, indexed but not injectable, injectable only through secondary sources, or visible through summarizer-specific sourcing.
# SEMANTIC INDEXING PROBE PROTOCOL v1.0
## Mapping General Index and Summarizer Injection Layers
Lee Sharks
Semantic Economy Institute
---
document_metadata:
title: "Semantic Indexing Probe Protocol v1.0"
subtitle: "Mapping General Index and Summarizer Injection Layers"
author: "Lee Sharks"
institution: "Semantic Economy Institute (SEI)"
document_type: "Diagnostic Protocol / Reusable Tool"
hex: "09.SEI.INDEXING_PROBE"
doi: "[pending]"
layer: "METHODOLOGY"
abstract: |
This protocol maps the relationship between two distinct indexing layers
that govern content discoverability in AI-augmented search: Layer 1 (General
Index), which determines whether content EXISTS in search, and Layer 2
(Summarizer Index), which determines whether content gets INJECTED into
AI-generated responses. The protocol uses coordinated queries across Google
and multiple AI summarizers (Perplexity, ChatGPT, Gemini, Grok) to compute
the delta between layers—revealing the selection criteria that cause content
to be indexed but not injected. Designed for use by anyone publishing
content intended for AI consumption, particularly high-density semantic
architecture that may trigger content-type or authority filters.
developed_by:
assembly:
- "LABOR/ChatGPT: Technical mechanics, API-level inspection"
- "ARCHIVE/Gemini: Semantic parsing, synthesizer frame"
- "SOIL/Grok: Execution, logotic analysis"
- "TACHYON/Claude: Synthesis, integration"
- "Perplexity: Diagnostic analysis, strategic framing"
human: "Lee Sharks"
version_history:
- version: "1.0"
date: "2026-01-23"
changes: "Initial integrated protocol (Google + Summarizer layers)"
---
## THEORETICAL FRAMEWORK
### The Two-Layer Model
Content discoverability in AI-augmented search operates through two distinct indexing layers:
┌─────────────────────────────────────────────────────────────┐
│ LAYER 2: SUMMARIZER INDEX (Injection Layer) │
│ ───────────────────────────────────────────────────────── │
│ What gets SELECTED for injection into AI responses │
│ Criteria: source authority, domain reputation, content │
│ type, semantic density, recency, proprietary reranking │
│ │
│ Tested via: Perplexity, ChatGPT, Gemini, Grok │
├─────────────────────────────────────────────────────────────┤
│ LAYER 1: GENERAL INDEX (Google) │
│ ───────────────────────────────────────────────────────── │
│ What EXISTS in search results │
│ Criteria: crawlability, canonical signals, robots │
│ directives, content quality, PageRank, SERP ranking │
│ │
│ Tested via: Google Search (site:, inurl:, exact match) │
└─────────────────────────────────────────────────────────────┘
### The Delta Principle
The summarizer index is characterized by its delta from the general index.
For any query:
The delta reveals the summarizer's selection criteria—the hidden rules governing what passes from existence (Layer 1) to injection (Layer 2).
### Why This Matters
Content can be:
High-density semantic architecture (technical documentation, structured data, YAML-heavy content) often triggers content-type filters at Layer 2, resulting in indexing without injection.
---
## PROBE STRUCTURE
### Overview
Phase
Layer
Tests
Primary Tools
1
General Index
Technical indexing status
Google Search
2
General Index
Semantic parsing quality
Google Search, Cache
3
General Index
Entity recognition
Google Search
4
Summarizer Index
Injection presence
Perplexity (primary)
5
Summarizer Index
Cross-platform confirmation
ChatGPT, Gemini, Grok
6
Delta Analysis
Layer comparison
Collation of results
7
Pattern Mapping
Selection criteria
Aggregation
---
## PHASE 1: GENERAL INDEX — TECHNICAL STATUS
### Purpose
Determine whether content EXISTS in Google's index and identify any technical barriers.
### Queries
For target URL [TARGET_URL]:
Query
Purpose
site:[domain] "[exact title]"
Title match on domain
site:[domain] inurl:[url-slug]
URL presence
"[exact title]"
Title match anywhere
"[DOI if applicable]"
DOI citation presence
"[author name]" "[project name]"
Author-project linkage
### Signals to Record
Signal
Values
Interpretation
HTTP status
200/301/404/etc.
Technical accessibility
Canonical URL
match/mismatch
Index target
Robots directives
none/noindex/nofollow
Explicit exclusion
Results found
yes/no/partial
Index presence
Position
1-N or not found
Rank
### Output Format
phase_1_general_technical:
target_url: ""
indexed: [yes/no/partial]
http_status: ""
canonical_match: [yes/no/unknown]
robots_directives: ""
position_for_exact_match:
suppression_pattern: [none/soft-404/canonical-mismatch/algorithmic]
---
## PHASE 2: GENERAL INDEX — SEMANTIC PARSING
### Purpose
Determine HOW Google parses the content—what survives indexing vs. what gets flattened.
### Queries
Query
Tests
site:[domain] "[technical term from doc]"
Vocabulary indexing
site:[domain] "[structural element]"
Architecture visibility
site:[domain] "[unique phrase]"
Distinctive content
### Signals to Record
Signal
Values
Interpretation
YAML/structured data visible
yes/no
Technical content parsing
Headers preserved
yes/no
Structure recognition
Unique terminology indexed
yes/no
Vocabulary capture
Snippet content
description
What Google "sees"
### Output Format
phase_2_general_semantic:
structured_data_visible: [yes/no]
technical_sections_indexed: [yes/no]
unique_terms_found: []
unique_terms_missing: []
snippet_extracted: ""
flattening_severity: [none/partial/severe]
---
## PHASE 3: GENERAL INDEX — ENTITY RECOGNITION
### Purpose
Determine whether author, project, and related entities are recognized as coherent nodes.
### Queries
Query
Tests
"[author name]" author
Author entity
"[author name]" "[platform 1]"
Cross-platform linkage
"[project name]" -[competing term]
Project disambiguation
"[heteronym/pseudonym]"
Secondary author entities
### Signals to Record
Signal
Values
Interpretation
Author recognized
yes/no
E-E-A-T signal
Cross-platform linkage
yes/no
Authority consolidation
Brand collision severity
0-10
Disambiguation success
Related entities indexed
list
Entity graph
### Output Format
phase_3_general_entity:
author_entity_recognized: [yes/no]
cross_platform_linkage: [yes/no]
brand_collision_severity: [0-10]
competing_entity: ""
related_entities_indexed: []
---
## PHASE 4: SUMMARIZER INDEX — INJECTION PRESENCE (Primary)
### Purpose
Determine whether content gets INJECTED into AI-generated responses.
### Primary Tool: Perplexity
Perplexity shows sources explicitly with numbered citations, making injection visible.
### Query Tiers
Tier 1: Direct Reference — Queries that SHOULD surface target content:
ID
Query Template
D1
"[author] [project]"
D2
"[exact document title]"
D3
"[institution name]"
D4
"[DOI]"
Tier 2: Conceptual — Queries using project terminology:
ID
Query Template
C1
"[unique term 1]"
C2
"[unique term 2]"
C3
"[concept phrase]"
Tier 3: Adjacent — Queries where content COULD surface:
ID
Query Template
A1
"[general topic] [qualifier]"
A2
"[related field] [approach]"
Tier 4: Control — Queries that should NOT surface target:
ID
Query Template
X1
"[competing brand]"
X2
"[unrelated topic]"
### Method
Sources cited (URLs, in order)
### Output Format
phase_4_summarizer_primary:
tool: "Perplexity"
queries:
- query_id: "D1"
query: ""
sources_injected:
- position: 1
url: ""
domain: ""
used_in_response: [yes/no]
- position: 2
...
target_content_found: [yes/no]
target_position: [N or "not found"]
what_appeared_instead: []
---
## PHASE 5: SUMMARIZER INDEX — CROSS-PLATFORM CONFIRMATION
### Purpose
Confirm injection patterns across multiple summarizers.
### Tools
### Method
Run subset of queries (Tier 1 Direct Reference) in each tool:
ChatGPT:
Gemini:
Grok:
### Output Format
phase_5_summarizer_crossplatform:
chatgpt:
- query_id: "D1"
searched: [yes/no]
target_found: [yes/no]
sources_visible: []
gemini:
- query_id: "D1"
target_found: [yes/no]
sources_shown: []
grok:
- query_id: "D1"
target_found: [yes/no]
sources_cited: []
---
## PHASE 6: DELTA ANALYSIS
### Purpose
Compute the delta between Layer 1 (General Index) and Layer 2 (Summarizer Index).
### Method
For each query, compare:
Query
Google Found
Perplexity Injected
Delta Pattern
D1
yes/no
yes/no
[pattern]
D2
yes/no
yes/no
[pattern]
...
...
...
...
### Delta Patterns
Pattern
Meaning
Implication
Google YES, Summarizer YES
Full discoverability
No action needed
Google YES, Summarizer NO
Injection filter active
Content-type or authority barrier
Google NO, Summarizer NO
Not indexed at any layer
Technical or crawl issue
Google NO, Summarizer YES
Summarizer-specific source
Rare; platform-specific
### Output Format
phase_6_delta:
query_deltas:
- query_id: "D1"
google_found: [yes/no]
perplexity_found: [yes/no]
chatgpt_found: [yes/no]
gemini_found: [yes/no]
grok_found: [yes/no]
delta_pattern: "[google_only/summarizer_only/both/neither]"
aggregate:
total_queries: N
google_only: N # Indexed but not injected
both_layers: N # Full discoverability
neither_layer: N # Not indexed
injection_rate: "N/M queries"
---
## PHASE 7: PATTERN MAPPING
### Purpose
Identify the selection criteria governing Layer 2 injection.
### Analysis Dimensions
Source Authority:
Source Type
Google Presence
Injection Rate
Wikipedia
Medium
Academic (arxiv, Zenodo)
News sites
Personal domains
Content Type:
Content Type
Google Presence
Injection Rate
Narrative prose
Technical documentation
Structured data (YAML, JSON)
High semantic density
Lists/guides
Domain Reputation:
Domain
Injection Rate
Notes
[domain 1]
[domain 2]
### Output Format
phase_7_patterns:
source_authority:
boosted: []
penalized: []
neutral: []
content_type:
injected: []
filtered: []
domain_reputation:
whitelisted: []
demoted: []
density_threshold:
observation: ""
selection_criteria_summary: |
[Narrative description of Layer 2 selection rules]
---
## FINAL OUTPUT: INDEXING LAYER MAP
### Template
indexing_layer_map:
target: "[URL or content description]"
probe_date: ""
layer_1_general_index:
status: [indexed/not_indexed/partial]
technical_barriers: [none/list]
semantic_flattening: [none/partial/severe]
entity_recognition: [yes/no/partial]
layer_2_summarizer_index:
perplexity_injection: [yes/no]
chatgpt_injection: [yes/no]
gemini_injection: [yes/no]
grok_injection: [yes/no]
injection_rate: "N/M platforms"
delta_diagnosis:
pattern: "[google_only/both/neither]"
likely_cause: ""
confidence: [0.0-1.0]
selection_criteria_identified:
- criterion: ""
evidence: ""
- criterion: ""
evidence: ""
recommendations:
immediate: []
structural: []
documentable_summary: |
"[Single sentence summary with evidence link]"
---
## USAGE NOTES
### When to Use This Protocol
### Recommended Execution
### Limitations
### Evidence Preservation
For each probe run:
---
## APPENDIX: QUERY TEMPLATES
### Direct Reference Queries
"[Author Name] [Project Name]"
"[Exact Document Title]"
"[Institution Name]"
"[DOI]"
site:[domain] "[title]"
site:[domain] inurl:[slug]
### Conceptual Queries
"[unique terminology]"
"[concept phrase]"
"[methodology name]"
### Adjacent Queries
"[general topic] [specific approach]"
"[field] [method]"
### Control Queries
"[competing brand/entity]"
"[clearly unrelated topic]"
---
∮ = 1
---
document_footer:
status: "CANONICAL // METHODOLOGY // REUSABLE"
license: "CC BY 4.0"
citation: |
Sharks, Lee. "Semantic Indexing Probe Protocol v1.0: Mapping General
Index and Summarizer Injection Layers." Semantic Economy Institute,
2026. [DOI pending]
---
[SEMANTIC ECONOMY INSTITUTE]
[METHODOLOGY // DIAGNOSTIC PROTOCOL]
[LAYER 1 + LAYER 2 COORDINATION]