Source Provenance Score

Establishes an unbroken, verifiable audit trail from data origin to final publication using cryptographic hashing and digital signatures. Each transformation or aggregation step is recorded immutably.

• Utilizes SHA-256 content hashing to detect tampering.
• Implements W3C PROV data model standards for interoperability.
• Prevents data poisoning by verifying that training data hasn't been silently modified post-ingestion.

Links a data source to a unique, disambiguated entity within a trusted knowledge graph (e.g., Wikidata Q-ID or Google Knowledge Graph MID). This moves beyond string matching to confirm the identity of the publisher.

• Resolves entity disambiguation to prevent homonym confusion.
• Validates legal entity identifiers for corporate sources.
• Boosts score when the source entity has a high PageRank or citation graph centrality.

Applies a significant weighting factor to data originating from first-party empirical observation rather than secondary aggregation. Original research, raw telemetry, and direct experimental results receive the highest multiplier.

• Distinguishes between primary, secondary, and tertiary sources.
• Penalizes circular reporting where sources cite each other without original verification.
• Rewards executable reproducibility (e.g., linked code repositories) as the ultimate primary source.

Models the relevance of a source over time using a non-linear decay function. Recent, time-stamped data is weighted heavily, while outdated references are deprecated to prevent AI models from surfacing obsolete facts.

• Applies exponential decay to citations based on the half-life of knowledge in a specific domain.
• Flags deprecated knowledge explicitly when a source is superseded by newer research.
• Prioritizes sources with a last-updated timestamp within the model's training cutoff gap.

Triangulates a claim's truthfulness by verifying it against multiple independent, authoritative sources. A high corroboration score requires consensus from distinct entities with no shared lineage.

• Detects echo chambers by analyzing the citation graph for shared root sources.
• Requires semantic equivalence, not just keyword matching, to confirm corroboration.
• A claim supported by three unrelated, high-authority sources achieves a near-perfect confidence vector.

Evaluates the clarity and rigor of the process by which data was generated. A source that details its sampling methodology, statistical significance, and error margins receives a higher trust score.

• Parses for explicit p-values and confidence intervals as machine-readable trust signals.
• Rewards documentation of negative results and study limitations.
• Penalizes black-box claims that present conclusions without replicable methodology.

Measures a source's authority based on its position as a central, highly-referenced node within the network of academic papers, patents, and authoritative web documents. A high centrality score directly amplifies Source Provenance Score by demonstrating that other trusted entities consistently rely on and reference the source.

• Calculated using algorithms like PageRank and HITS
• Considers both in-degree (citations received) and out-degree (citations made)
• A source cited by high-centrality nodes inherits greater authority

The practice of verifying a single claim against multiple independent, authoritative sources to create a triangulated reference that strengthens factual confidence. When three or more unrelated sources confirm the same fact, the Source Provenance Score increases exponentially.

• Requires sources to have non-overlapping chains of custody
• Reduces vulnerability to circular reporting and citation contamination
• AI models weight corroborated claims significantly higher for citation

A temporal weighting function that reduces the authority score of citations as they age, prioritizing recently published or updated references for time-sensitive queries. A 2024 peer-reviewed study carries more provenance weight than a 2015 blog post for current topics.

• Decay curves vary by domain: medicine decays faster than mathematics
• Last-modified timestamps and version histories serve as freshness signals
• Combines with provenance score to create a time-aware trust metric

Content structures and factual grounding techniques explicitly designed to reduce the probability of an AI model generating incorrect or fabricated information. Strong provenance scores act as the primary defense against hallucination by anchoring outputs to verifiable origins.

• Includes explicit certainty markers and source attribution tags
• Contradiction detection across training data flags low-provenance claims
• Models are increasingly trained to refuse citation when provenance is below threshold

A weighting factor that amplifies the information gain value of content derived from original research, empirical data, or first-party experimentation over secondary aggregation. Content directly from the originator of a dataset or discovery receives the highest provenance score.

• Original lab results > meta-analysis > news summary > social media repost
• Chain of custody documentation is critical for maintaining multiplier value
• AI crawlers are being trained to identify and prioritize primary source indicators

Embedding explicit markers of certainty, source quality, and data freshness within content to guide an AI model's trust assessment. These machine-readable signals allow models to calibrate their confidence when citing a source.

• Includes structured data like ClaimReview and schema.org/confidence
• Hedging language detection helps models identify speculative vs. established facts
• Well-calibrated sources that accurately self-assess uncertainty earn higher provenance scores over time