Overview

@disclosureos/scoring answers the fifth question the standard asks: how complete and compelling is the case?

It is the consumer layer — it reads the enriched record produced by the other three and computes reference measures. It augments nothing and stores nothing on the record.

npm install @disclosureos/scoring

Two orthogonal measures

The separation is deliberate. A thoroughly documented mundane sighting scores high on completeness and near zero on compellingness. A thin but extraordinary radar case scores the reverse. Collapsing those into one number would hide exactly what a reader needs to know — so the framework doesn't.

import { getCompleteness, score } from '@disclosureos/scoring';

const completeness = getCompleteness(observation);
completeness.percentage;          // 0–100: coverage of all schema fields
completeness.requiredPercentage;  // 0–100: coverage of required fields
completeness.missing;             // exactly which paths are absent

const result = score(observation);
result.score;       // 0–1 consensus point estimate
result.range;       // { low, high } — spread across competing claims
result.contested;   // do evaluators disagree on direction?
result.components;  // per-signal breakdown: technology, biologics, origin

Design commitments

• No single headline score. Multiple measures, each answering one question.
• Transparent methodology. The weights are exported data (DEFAULT_WEIGHTS), the formula is open source, and every result is stamped with scoringVersion.
• Disagreement is surfaced, not averaged away. Competing claims produce a range and a contested flag alongside the point estimate.
• Prosaic means explained. A confident prosaic origin classification contributes zero anomaly signal — the scorer can't be gamed by enthusiastically classifying a case as a balloon.
• Overridable, not configurable. Weights, prosaic prefixes, and evaluator trust are plain function options. Institutions can publish alternative methodologies as data, not forks.

Ranking

import { rankByCompellingness } from '@disclosureos/scoring';

const ranked = rankByCompellingness(observations);
// most compelling first — the "one case that matters" surfaces

Subpaths

import { getCompleteness } from '@disclosureos/scoring/completeness';
import { score, rankByCompellingness } from '@disclosureos/scoring/compellingness';