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:

• If content appears in Google but NOT in summarizer injection → Injection filter active
• If content appears in summarizer but NOT in Google → Summarizer-specific sourcing (rare)
• If content appears in both → Full discoverability
• If content appears in neither → Not indexed at either layer

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:

• Indexed but not injectable — Exists in search, never appears in AI responses
• Injectable from limited sources — AI cites Wikipedia/Reddit discussing your content, not your content directly
• Fully discoverable — Appears in both search results and AI injection

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

• Open Perplexity (fresh session)
• Enter query verbatim
• Record:

Sources cited (URLs, in order)

• Which sources used in response text
• Whether target content appears
• What appears INSTEAD

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

• ChatGPT (web browse mode)
• Gemini (with search grounding)
• Grok (DeepSearch mode)

Method

Run subset of queries (Tier 1 Direct Reference) in each tool:

ChatGPT:

• New conversation, browsing enabled
• Enter query; if no search, prompt "Can you search for [query]?"
• Record sources cited

Gemini:

• Ensure web grounding enabled
• Enter query
• Record source chips shown

Grok:

• Enable DeepSearch/real-time
• Enter query
• Record sources cited

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

Reddit

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

• Publishing content intended for AI consumption
• Diagnosing why content appears in search but not AI responses
• Mapping selection criteria for high-density semantic architecture
• Understanding platform-specific injection patterns

Recommended Execution

• Primary executor: Perplexity (explicit source citation)
• Secondary confirmation: ChatGPT, Gemini, Grok
• Baseline: Google Search (logged out, incognito)
• Frequency: Re-run 48-72 hours after publication to allow crawl propagation

Limitations

• Summarizer behavior varies by session/user/time
• Some criteria are proprietary and unobservable
• Protocol maps symptoms, not source code
• Results are diagnostic, not definitive

Evidence Preservation

For each probe run:

• Screenshot results with timestamp visible
• Archive target URL (archive.org, archive.ph)
• Save raw query results
• Document tool versions and modes used

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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

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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,

• [DOI pending]

---

[SEMANTIC ECONOMY INSTITUTE]

[METHODOLOGY // DIAGNOSTIC PROTOCOL]

[LAYER 1 + LAYER 2 COORDINATION]