Scoring methodology
Everything about how Sigñal measures the gap between what brands claim and what they visibly support — published in full. Any brand, researcher, or journalist can audit this. Nothing is hidden.
Overview
Signal is a claims-intelligence and evidence-alignment system. It measures the gap between what wellness brands publicly claim, what evidence they visibly provide, and how clearly those claims are connected to that evidence.
The Evidence Score (0–100) reflects that alignment. Higher score = stronger evidence alignment between what a brand claims and what it visibly supports. The score is composite, weighted, location-sensitive, and category-adjusted. It is calculated every time a page is scanned and stored in the Sigñal Index.
The formula
// internal gap measure, clamped to 0–100 (higher = worse alignment)
base_gap = Σ (dimension_gapi × weighti)
// 60% deterministic + 40% AI analysis, blended per dimension
location_factor = 0.5 + (avg_claim_location_multiplier / 3.0) × 1.0
intensity_penalty = Σ (intensity_level × (100 − visible_support_score) × 0.15) × (location_mult / 3.0)
category_addons = Σ category_specific_checks // capped at +15 total
Evidence Score = 100 − gap_score // displayed score: higher = better
Worked example
A supplement brand with ingredient-level studies on a /science page, but no finished-product trial and a hero claim of "clinically proven" with no visible citation from the product page.
Evidence Alignment 48 × 0.25 = 12.0
Claim Clarity 42 × 0.15 = 6.3
Evidence Quality 52 × 0.15 = 7.8
Consumer Distortion Risk 38 × 0.15 = 5.7
Claim Strength 46 × 0.10 = 4.6
Evidence Accessibility 55 × 0.10 = 5.5
base_gap = 41.9 // Step 2: location factor — hero headline + benefit bullets dominant
location_factor = 1.15 // weighted avg of zone multipliers
41.9 × 1.15 = 48.2
// Step 3: intensity penalty — "clinically proven" (high intensity) with no hero-page citation
intensity_penalty = +2.8
// Step 4: category add-ons — supplements: no finished-product trial visible (+3)
category_addons = +3.0
gap_score = 48.2 + 2.8 + 3.0 = 54
Evidence Score = 100 − 54 = 46 → Yellow Signal
// Ingredient studies exist but the finished-product "clinically proven" claim has no visible support
Six dimensions
Each dimension measures a specific aspect of evidence alignment and receives an internal gap score from 0–100 (higher = larger gap between claims and visible support). These sub-scores feed the formula above and are inverted in the final Evidence Score. Scores are blended from deterministic rules (60%) and AI analysis (40%).
| Dimension | Weight | What it measures |
|---|---|---|
| Evidence Alignment | 25% | How well visible evidence matches the strength and specificity of claims made. A brand with peer-reviewed studies clearly connected to its claims scores well here regardless of where those studies are hosted on the domain. |
| Claim Clarity | 15% | How precisely claims are stated — whether outcomes are quantified, durations specified, and subject populations identified. Vague language with no visible support scores higher risk than specific language with visible backing. |
| Evidence Quality | 15% | The verifiability of cited sources — clickable study links, author names, DOIs, COA links. Evidence that exists but cannot be inspected is scored differently from evidence that is fully transparent. |
| Consumer Distortion Risk | 15% | Combined signal of regulatory proximity (disease-treatment language), comparison fairness ("3× faster" without a baseline), and trust marker integrity (badges or testimonials used as proof without a link to underlying evidence). |
| Claim Strength | 10% | How plausibly the described biological or physiological mechanism is grounded in visible references. A mechanism claim with no citations scores higher risk than one with linked human trial data. |
| Evidence Accessibility | 10% | How easily a consumer can locate the supporting evidence from the page they are reading. Evidence buried several clicks away or hidden behind login gates scores higher than evidence linked directly from the claim. |
Location multipliers
Claims are weighted by where they appear on the page. A "clinically proven" claim in the hero headline carries 6× more weight than the same claim buried in the footer, because that's where brands put the claims they most want you to believe.
Claim intensity ladder
Claims are classified by intensity. Higher-intensity language with lower visible support generates a proportionally larger penalty — because the gap between what's claimed and what's shown is wider.
Category-specific add-ons
Each product category has additional risk checks applied after the base score. These are capped at a combined +15 total to the final score.
Supplements
- Dosage transparency missing — no Supplement Facts panel visible
- No third-party testing certificate visible (NSF, Informed Sport, USP, COA)
- Ingredient-level evidence used in place of finished-product evidence
- No author names or citation links visible alongside study claims
Neurotech & brain devices
- Stimulation parameters not disclosed (Hz, mA, duration, waveform)
- EEG or neurofeedback accuracy claims without visible validation
- Medical vs. wellness boundary language without appropriate disclaimer
Recovery devices (red light, PEMF, cold therapy, massage)
- Wavelength, irradiance, or protocol not specified on page
- Product-specific evidence missing — ingredient evidence ≠ device evidence
- Comparison to medical devices without substantiation
Wearables & trackers
- Sensor accuracy % claims without visible validation studies
- Algorithm or AI-based insight claims without transparency
Confidence score
Separate from the Evidence Score, confidence reflects how reliably the scan captured the page's content. Low confidence forces a "Limited Signal" state regardless of score.
- High — page fully extracted, claims clearly identifiable, content complete
- Medium — partial extraction or some ambiguity in claim language
- Low — thin content, JavaScript-heavy page, or insufficient signal to score reliably
Ranking
Within each category, products are sorted by Evidence Score descending — highest score = Rank #1. Tie-breakers in order:
- Higher confidence score
- More product-specific evidence visible
- Better source transparency
- More recent scan
Low-confidence scans are placed at the bottom with "Rank Pending" until confidence improves.
Signal thresholds
The Evidence Score maps to three signals. Signal does not tell consumers what to buy. Signal shows what holds.
- Green (70–100) — claims appear proportionate to available evidence. Visible support is present and clearly connected.
- Yellow (45–69) — evidence exists, but is not clearly connected to all claims. Gaps are present and notable.
- Red (0–44) — claims extend beyond visible support. The gap between what is claimed and what is shown is material.
The role of AI
The scoring engine is deterministic — parsing, weighting, math, caching, and ranking are all rule-based and fully auditable. Claude (Anthropic) provides the analyst layer only: nuanced language understanding, per-dimension scores, and human-readable explanations.
Claude's scores are blended in at 40% weight per dimension. The deterministic engine contributes 60%. No score is determined solely by AI output — and all Claude responses are validated against Signal's language requirements before being stored or displayed.
Signal's AI layer operates under strict language constraints. It is never permitted to speculate about intent, render verdicts about honesty, or use: fraud, scam, lie, fake, false, illegal, deceptive, misleading (as a verdict), noncompliant, or "doesn't work."
Approved language includes: "This claim extends beyond visible support", "Evidence exists, but is not clearly connected to this claim", "Claims appear proportionate to available evidence", "visible support appears limited", "source transparency appears incomplete."
What Signal does not measure
Signal measures the gap between what brands claim, what evidence they provide, and how clearly those claims are connected to that evidence. Nothing more.
A red signal means claims extend beyond visible support on the page at the time of scan — not that a product is unsafe, ineffective, or that any intent to mislead exists.
- Product efficacy — whether the product delivers its promised outcome
- Clinical safety — whether ingredients or devices are safe to use
- Ingredient quality — whether underlying ingredients are effective at any dose
- Manufacturing standards — GMP compliance, facility audits, contamination risk
- Regulatory compliance — whether claims violate FTC, FDA, or other regulations
- Company intent — Signal does not assume or assess intent
Disputes & corrections
Sigñal is built on public data crawled at a point in time. Crawlers have limits: JavaScript-rendered pages may be partially captured, evidence hosted on subdomains may be missed, and content changes after a scan are not reflected until the next scan.
If a score is materially wrong — because our crawler missed a /science page, misread a claim, or the page has changed — brands and third parties can request a correction.
How to dispute a score
- Email signal@bhvd.com with the subject line Score Dispute — [domain]
- Describe what was missed or misrepresented and where the correct evidence is publicly accessible
- Scores are updated only when the supporting evidence is verifiably visible on the live consumer-facing page — not when evidence is emailed to us
- We aim to acknowledge dispute emails within 5 business days
Update cadence
- New scans — immediate provisional rank on first scan
- High-traffic categories (supplements, skincare, recovery) — ranks recalculated hourly
- All other categories — ranks recalculated daily
- Brand evidence submissions — reviewed by humans; scores update only when evidence becomes visible on the live consumer-facing page
- Cache TTL — 6 hours for known pages, 1 hour for new entries
Questions about the methodology? Email signal@bhvd.com.
Version 1.3 · Last updated April 2026 · Sigñal