/**
 * Scrape score card — numeric confidence across 4 dimensions.
 *
 *   1. Completeness         — did we get ALL the stores?
 *   2. Data quality         — is each record richly populated?
 *   3. Source authenticity  — is this a real brand-controlled source?
 *   4. Freshness            — how recent is the data?
 *
 * Each dimension is 0-100. The overall score is a weighted composite.
 * Every score is backed by EVIDENCE — each check shows what it found,
 * its weight, and how it contributed. No black-box numbers.
 *
 * Designed to be auditable: a low score must point to specific recommendations.
 */
import type { RetailStore } from "../retail.js";
export interface Evidence {
    /** Short name of the check. */
    readonly check: string;
    /** Human-readable result, with numbers when relevant. */
    readonly result: string;
    /** Weight in this dimension's score (sum = 1.0 per dimension). */
    readonly weight: number;
    /** 0-100 contribution. */
    readonly score: number;
    /** "info" | "warn" | "fail" — for flagging. */
    readonly severity: "info" | "warn" | "fail";
}
export interface DimensionScore {
    /** 0-100. */
    readonly score: number;
    readonly evidence: readonly Evidence[];
}
export interface ScrapeScoreCard {
    readonly brand: string;
    readonly extracted: number;
    /** Composite 0-100. */
    readonly overall: number;
    /** Confidence band derived from overall score. */
    readonly band: "high" | "medium" | "low";
    readonly completeness: DimensionScore;
    readonly dataQuality: DimensionScore;
    readonly sourceAuthenticity: DimensionScore;
    readonly freshness: DimensionScore;
    /** Anything the score card thinks the human should look at. */
    readonly flags: readonly string[];
    /** Concrete next steps. */
    readonly recommendations: readonly string[];
}
export interface BrandContext {
    readonly brand: string;
    /** Source's own claim (e.g. API returns store_count). */
    readonly sourceClaimedCount?: number;
    /** Manually-curated count from public source (Wikipedia / annual report). */
    readonly authoritativeCount?: number;
    /** Cite the public source for the authoritative count. */
    readonly authoritativeCitation?: string;
    /** Acceptable drift from authoritative. Default 15%. */
    readonly tolerancePct?: number;
    /** Cities the brand is known to have stores in (public press). */
    readonly expectedCities?: readonly string[];
    /**
     * Major metros for the metro-coverage completeness check. Defaults to
     * India's six metros — set this when scoring a brand in another region.
     */
    readonly expectedMetros?: readonly string[];
    /**
     * Bounding box for the coordinate-plausibility quality check. Defaults to
     * India — set this when scoring a brand in another region.
     */
    readonly geoBounds?: {
        readonly minLat: number;
        readonly maxLat: number;
        readonly minLng: number;
        readonly maxLng: number;
    };
    /** Source URL / API host. */
    readonly sourceDomain: string;
    /** Brand's primary domain (for the source-authenticity check). */
    readonly officialDomain: string;
    /**
     * Sibling-brand probes the recon discovered. If any returned non-empty
     * BUT we didn't extract them, that's an exhaustiveness gap.
     */
    readonly siblingBrandsProbed?: readonly {
        name: string;
        expectedCount: number;
        extracted: boolean;
    }[];
    /**
     * Manually-asserted authenticity signals. Used because we can't reliably
     * detect "is this a real company" programmatically — but we CAN detect
     * "the domain matches a public brand" etc.
     */
    readonly authenticitySignals?: {
        readonly hasWikipediaArticle?: boolean;
        readonly publicCompany?: boolean;
        readonly knownBrand?: boolean;
        readonly domainMatchesBrand?: boolean;
    };
}
export declare function scoreCard(stores: readonly RetailStore[], ctx: BrandContext): ScrapeScoreCard;
export declare function formatScoreCard(card: ScrapeScoreCard): string;