Evergreen Score

The information within the asset is based on immutable truths or foundational principles that do not require annual revision. The content avoids reliance on volatile statistics.

• Principle-Based Knowledge: The content teaches concepts (e.g., 'Newton's Laws') rather than reporting transient data (e.g., 'current GDP').
• Low Content Diff Ratio: Automated Delta Detection Engines find minimal semantic differences between the current version and a version from 12 months prior.
• No Broken References: The asset contains zero links to deprecated resources or 404 errors, preventing Content Rot Detection flags.

The asset is correctly tagged as Semi-Evergreen Classification if it requires minor, predictable updates. A high score is maintained if the update cadence is slow and the core structure remains untouched.

• Predictable Refresh Triggers: Updates are tied to known cycles (e.g., annual tax code changes) rather than breaking news.
• Stable Core Structure: The HTML template and primary headings remain constant; only isolated data points change.
• Automated Update Pipeline: Minor data refreshes are handled programmatically without full re-writes, preserving historical authority.

The URL functions as a definitive hub for a specific topic, accumulating references over time. High scores correlate with assets that have become the canonical source.

• Link Graph Stability: The page serves as a persistent node in the Internal Link Graph Automation, receiving consistent internal links from newer, more ephemeral content.
• High Citation Integrity: External sources cite the page as a definitive reference, contributing to a stable Backlink Velocity.
• Topical Depth: The content comprehensively covers the entity, leaving no 'information gap' that a fresher competitor could exploit.

The content maintains a stable vector position in semantic space. A Semantic Drift Monitor confirms that successive minor edits do not shift the document's core meaning away from the target entity.

• Stable Entity Salience: The primary entities (people, places, concepts) identified by natural language processing remain dominant in every revision.
• Consistent Topical Clusters: The subtopics covered remain tightly clustered around the main subject without wandering into adjacent, less relevant areas.
• Preserved Keyword Density: The distribution of critical terminology remains statistically consistent over time, preventing confusion for search engine classifiers.

A content categorization that identifies assets requiring periodic but infrequent updates to prevent slow decay into staleness. Unlike truly evergreen content, semi-evergreen assets contain elements with a defined shelf life.

• Example: A 'State of DevOps' report that needs annual statistic refreshes
• Update cadence: Typically quarterly or yearly refresh cycles
• Risk: Often mistaken for fully evergreen, leading to silent decay
• Detection: Monitored via Content Staleness Index thresholds

A composite metric that quantifies the degree to which a document's information, references, or statistics have become outdated relative to the current factual consensus. This index serves as the inverse counterpart to the Evergreen Score.

• Inputs: Publication date, citation freshness, broken link ratio, factual drift
• Output: A numerical score triggering Automated Refresh Triggers
• Use case: Prioritizing which assets to update first in large-scale content audits
• Relationship: High Evergreen Score = Low Staleness Index

A mathematical model defining the rate at which a content asset loses ranking authority over time. Different content types exhibit distinct decay curves that inform their Evergreen Score classification.

• Exponential decay: Breaking news loses 90% of value within 48 hours
• Linear decay: Product reviews degrade steadily over 12-18 months
• Step-function decay: Documentation becomes obsolete instantly upon version releases
• Application: Used to calculate Time-Decay Weighting in ranking algorithms

A natural language processing model that analyzes a search query to determine if the user requires the latest information, a specific historical snapshot, or timeless knowledge. This classifier determines whether an Evergreen Score matters for a given query.

• Query types: 'QDF-sensitive' (needs freshness) vs. 'Evergreen-stable' (needs authority)
• Signal examples: '2024 election results' triggers recency; 'Pythagorean theorem' does not
• Impact: Pages with high Evergreen Scores are preferentially served for timeless queries
• Related: Directly influences Query Deserves Freshness (QDF) activation

A governance designation defining whether an asset is in a creation, peak performance, decay, or archival phase. The Evergreen Score is a key input for automating lifecycle transitions.

• Stages: Creation → Peak Performance → Decay → Archival
• Automation: High Evergreen Score assets skip decay monitoring; low scores trigger Automated Refresh Triggers
• Governance: Dictates automated update or deprecation rules in Programmatic Content Governance pipelines
• Outcome: Prevents resource waste on updating assets that should be retired

A specific component of a search engine's scoring algorithm that isolates and evaluates the 'age' of a document to determine its suitability for time-sensitive queries. The Evergreen Score predicts whether this rank will remain stable.

• Calculation: Based on publication date, last-modified timestamps, and update frequency
• Interaction: Works alongside Recency Boosting for new content
• Stability: Documents with high Evergreen Scores maintain consistent Freshness Rank without intervention
• Monitoring: Tracked via Keyword Decay Mapper diagnostics