---
title: How the scoring systems work
description: The platform's seven scoring axes — what each one measures, the idea behind its formula, and who consumes it — plus the rule that keeps scores from lying.
locale: en
version: 0.2.0
canonical: https://enterium-developer-docs.pages.dev/developers/how-it-works/scoring-systems/
---

> Documentation index: https://enterium-developer-docs.pages.dev/developers/llms.txt — every page of this portal has a Markdown twin at <route>.md; the frozen API contract is at https://enterium-developer-docs.pages.dev/developers/api/openapi.json.

**The problem:** a platform that decides autonomously needs numbers — but
a single "quality score" blending unrelated concerns is a number that
lies. The design principle here: **one score, one question, one
consumer** — and safety verdicts never hide inside averages.

```mermaid
mindmap
  root((Scoring))
    Candidate rank
      ontology overlap
      deep LLM analysis
    Dedup cosine
      collect gate
      publish gate
    Corpus coverage
      claim-value coverage
      slot coverage
    Article quality stamp
      structure
      AI-marker absence
      technical SEO
    Brand-safety signals
      verdict, not number
    Topic yield
      impressions per article
    Relevance
      cited-first membership
```

## The inventory

| Score | Question it answers | Formula idea | Consumer |
| --- | --- | --- | --- |
| **Candidate rank** | "Is this source worth an article?" | Structured ontology-overlap (tiered term comparison + domain affinity) blended with a deep LLM analysis into 0–100 | The export gate's floor and bands |
| **Dedup cosine** | "Have we already covered this?" | Embedding cosine of the candidate/article against committed topics and batch peers; one calibrated scale for both the collect-side and publish-side gates | Defer verdicts |
| **Corpus coverage** | "Does the corpus know enough to write this niche?" | Weighted blend: claim-value coverage (facts carrying usable numbers) and slot coverage (ontology-derived topic slots filled) | The research-floor and enrichment planning |
| **Quality stamp** | "How well-made is the artifact?" | Deterministic automatic categories (structure, AI-marker absence, technical SEO) scaled, minus severity-weighted defect penalties | Audits, remediation queues, reporting |
| **Brand-safety battery** | "May this ship under our brand?" | A set of independent regex-precise signals (competitor domains, names in titles, recommendation-verbs, third-party promo, buyers-guide shapes, outbound links, placeholder leaks) | The **verdict** — reject/rework — never the number |
| **Topic yield** | "Does this topic earn attention?" | Impressions per article over a maturity-filtered window, with sample-size guards | The [topic feedback loop](../topic-feedback-loop/) |
| **Relevance** | "May this fact be cited here?" | Cited-first membership — the references list contains only what the text actually used; a cross-encoder reranker orders candidates | The writing framework's retrieval |

## The two rules that keep scores honest

**1. Calibrate against labels, not vibes.** The dedup threshold moved only
after a manual labeling study (~a hundred pairs): the old threshold
measured at ~0.49 precision — a coin flip — the new one at ~0.83, and one
notch higher started losing true same-batch clones. Similarly, the corpus
coverage match threshold was recalibrated after the original value
saturated at 1.000 on virtually every run — a score that always says
"perfect" measures nothing.

**2. Verdicts don't hide in averages.** Brand-safety and grounding
failures flip the article's verdict directly. A "9/10 quality" article
with one competitor-promotion hit is not a 8.5 — it is a rejected
article with a high stamp, and both facts stay visible.

## Trade-offs

Threshold calibration is perishable: change the embedding model and every
cosine threshold silently means something else (an offline recalibration
protocol exists for exactly this migration). And regex-precise safety
signals trade recall for auditability — each signal is narrow and
explainable, at the cost of needing new signals as new failure shapes
appear; the signal battery grows by incident, deliberately.

## See it in two minutes

Any article row carries its stamp, signals and dedup scores in its
telemetry; any candidate carries its rank; the corpus coverage of a
contract is a first-class read on the `research` domain.