Pattern Breakdown Analyzer
See the raw pattern coefficients and signal drivers behind the AI content analysis. Useful for understanding exactly which features contribute to the detection.
Signal reference
Understanding detection signals
Each coefficient targets a specific linguistic dimension. Here's what the research says about the strongest signals.
Burstiness (sentence variance)
Human writing naturally varies sentence length significantly. AI tends to produce more uniform structures. Low variance is one of the most studied statistical signals in detection research.
Lexical predictability
AI-generated text tends to use more predictable word choices — common collocations and high-frequency phrases. GLTR operationalizes this by showing token-rank distributions.
Hedge-word density
Phrases like 'It's important to note' and 'Additionally, it's worth mentioning' appear at elevated rates in AI output. Individually weak; clustered, they become a strong signal.
Structure regularization
Uniform paragraph lengths, predictable section patterns, and templated list structures. Human writing is naturally more varied in structural organization.
Research context
Detection method tiers — where this tool fits
Text-only statistics
This toolPerplexity, entropy, burstiness, token-rank heuristics. Cheap and explainable but brittle to distribution shift and prone to bias on non-native English writing.
Zero-shot LM comparison
Curvature-based methods (DetectGPT, Binoculars) compare text against model likelihoods. Requires access to model computations — not possible for end-user text analysis.
Watermarking
Generation-time watermarks embed statistical signals during decoding. Requires generator cooperation and can degrade under editing.
Cryptographic provenance
Signed metadata and content credentials provide verifiable origin claims with near-zero false positives when signatures verify. Requires ecosystem adoption.
This tool operates at the text-only statistics tier — the weakest but most accessible method. It provides useful signals for content quality improvement but should not be used for high-stakes attribution decisions.
Common questions
Frequently asked questions
What are pattern coefficients?
Pattern coefficients are individual 0-100 scores measuring specific linguistic dimensions of your text. Each one targets a distinct pattern associated with AI-generated content: hedge-word density, benefit stacking, transitional adverb frequency, structural monotony, and more. They are computed deterministically — the same input always produces the same output.
How do I interpret the coefficient values?
0-34 (green): Low signal — this pattern is at or below human baseline levels. 35-59 (amber): Moderate signal — pattern is present at levels that may be AI-associated, but could also be legitimate for certain content types. 60-100 (red): Strong signal — this pattern is present at levels strongly associated with AI generation. Content type matters: a 50 for 'benefit_stacking' on a landing page is less concerning than a 50 on a blog post.
Why would a human-written text have high coefficients?
Several patterns overlap between AI and legitimate human writing. Marketing copywriters often use benefit stacking, CTAs, and organized structures — the same patterns AI tends to produce. Non-native English writers may use simpler sentence structures and common transitional phrases. Research documents that this overlap is a fundamental challenge for all text-only detectors.
What does the model resemblance distribution show?
It shows which AI model family the writing style most closely matches based on lexical and structural patterns. This is stylistic similarity, not identification. Research consensus is that reliable model attribution from output text alone is not dependable at high confidence — it requires provenance-based approaches (watermarks, signed metadata) for strong attribution.
Can I use this to train my own detector?
The coefficients provide a useful feature set, but building a reliable detector requires much more: calibrated confidence bands, content-type normalization, bias auditing (especially for non-native English writers), continuous evaluation against new model families, and OOD testing. The detection research community consistently emphasizes that evaluation frameworks remain weak and adversarial conditions are the main failure mode.
How is this different from the AI Content Checker?
The AI Content Checker provides a comprehensive diagnostic report optimized for action: classification, summary, recommendations, and passage-level rewrite hints. The Pattern Analyzer shows the raw signal data — individual coefficients with explanations, sorted by strength. It's designed for users who want to understand exactly what contributed to the detection result.
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