AI-powered evaluation using the Model Context Optimization BS Detection Framework, based solely on publicly available website content.
Based on 259 businesses audited.
Government, Municipal & Public Sector BS: U.S. Energy Information Administration (EIA) (eia.gov)
This is a gold-standard site for substance-over-signal design. It contains virtually no marketing bullshit, operating instead as a functional data utility for the energy sector.
Implement comprehensive Organization and DataDownload schema to improve machine readability of the statistical sets. Explicitly link Person schema for lead analysts in the Press Room to bridge the anonymous authority gap. Consolidate the navigation header to reduce heading repetition on high-density pages like the A-Z Index. Ensure all legacy reports (like those from 2007 noted in the index) are clearly archived to maintain the perception of extreme recency.
Information density is exceptionally high, with a near-zero percentage of fluff headings. Instead of power words, the site uses specific nouns like Petroleum, Natural Gas, and Electricity. The body text contains granular substance, citing exactly 43.7 billion cubic feet per day (Bcf/d) and specific forecast deltas for the summer of 2027.
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There is virtually no semantic drift between the homepage signal and the sub-page substance. The H1 promise of Independent Statistics and Analysis is immediately supported by the Short-Term Energy Outlook (STEO) on the homepage and the massive alphabetical data repository found in the A-Z Index. The hierarchy remains consistent across all 4 analyzed pages, focusing on data delivery over narrative persuasion.
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The site avoids trust theatre by functioning as the primary source of truth rather than relying on third-party validation. While review_count is technically non-zero, it appears to be internal feedback markers rather than marketing testimonials. All claims link directly to data browsers or primary reports like the STEO, providing a clear proof path.
Proof density is at maximum levels. The A-Z Index alone provides hundreds of links to specific data files, state profiles, and historical reports. The ratio of vague assertions to verifiable evidence is roughly 1:20, with almost every sentence containing a date, a metric, or a specific geographic entity.
To examine how structural entropy affects chunking and retrieval, review the Moz Semantic HTML audit. View the Moz Semantic HTML Audit for a complete example of heading logic, landmark integrity, and DOM depth diagnostics.
While the site uses industry-standard terms like open data and transparency, these are functional descriptors of the service provided rather than marketing cliches. The value proposition is entirely unique to a national statistical body and could not be copy-pasted onto a private sector competitor. Boilerplate sections are minimal and contain dynamic, specific content.
Authority is established through institutional weight and current timestamps (May 28, 2026, which is 1 day old relative to the anchor). The only gap is the absence of structured JSON-LD schema in the provided data, which would typically support the Organization and GovernmentService identity. Expert claims are tied to named reports rather than individual personalities, which is standard for government authority.
There is no disconnect between claims and delivery. The site claims to provide energy forecasts and immediately presents a chart for average summer natural gas consumption (2016-2027). Every performance-related assertion is backed by a specific data source, such as the May 2026 Short-Term Energy Outlook.
Government, Municipal & Public Sector BS: U.S. Energy Information Administration (EIA) (eia.gov)
The website is a perfect archetype for the Government and Public Sector category, specifically as a statistical agency. The content is entirely focused on public value through evidence-based policy data and the provision of open data for energy markets.
The access layer decides whether your content even enters the model's world. Review the Crawlability & Indexation Framework to see how AI visible content differs from what humans see in the browser.
“The score of 12 is driven primarily by minor technical gaps (lack of schema) and unavoidable industry jargon. The site scores near-zero for information density and semantic drift because it provides raw evidence for every claim made.”