Evidence provenance graph

Worked example

A researcher can trace several reports of the same delayed shipment back to the original record. Count separately verified observations only when their independence is documented.

Five fictional reports lead back to two traceable origins. One report still has an unknown origin. Read the source trail before deciding whether the reports independently confirm the claim.

Try it yourself

The calculator opens with the fictional example already filled in. Change one input, choose Calculate locally, and compare the result to the original values below. Use Reset fictional fixture to start over.

An unknown result means the available inputs do not support a value. Check the calculator’s Warnings tab for what is missing or uncertain.

Known traceable origin clusters 2 origin clusters
Reports with unknown origin 1 reports
Declared reports 5 reports
Traceable origins without supplied timestamps 2 origins

Go to the calculator Browse all calculators

Calculator

Approach (label only)Changing the label does not improve capacity, speed, error rates or confidence. Enter measured differences explicitly.

Comparison conditions and human review

Equal-resource comparison conditionsRecord budget, staffing, resources, information release dates, scenario conditions and evaluation criteria. Material differences can make options not comparable.

Evaluation period

Geography / exercise boundary

Human reviewers and roles

This evaluation depends on a real transaction

Supplied human authority gateThis declaration is not legal certification. Blocked, pending or assumed gates stop transaction-dependent evaluation.

Keep units consistent within each table. Enter zero when the amount is known to be zero. Leave optional amounts blank when they are unknown. If a required value is missing, the calculator will ask for it.

Advanced: complete input envelope and optional fields

Edit optional fields using the method dictionary. Apply validates and recalculates the entire input. Unknown values remain null.

Versioned input JSON

Import local JSON or CSV

Files stay in this browser. Maximum 1 MiB. JSON imports are recalculated. CSV supports flat observation tables, with column types taken from the displayed fixture. Empty cells become null.

Workbench JSON / input envelope

CSV target table

CSV file, matching the target table headers

Compare the arrangements

Keep physical resources and evaluation conditions equal. With identical inputs, all three approaches produce identical arithmetic. The comparison refuses material differences in declared conditions; it does not verify those declarations.

Show only differing metric rows

Privacy: comparison inputs may contain sensitive notes or operational details. Storage is off by default. Do not enter personal records or sensitive infrastructure details. Reproduction exports include supplied inputs and notes, including raw small-cell inputs suppressed from CORE-009 result tables. Suppression of displayed results does not make a reproduction export safe to publish.

Save comparison inputs on this device until I clear them

Executive brief and reproducible export

Public exercise notes

Review for personal, confidential or sensitive material before saving or sharing. No automatic submission occurs.

Read the generated brief

Method, inputs and limits

Trace declared report dependencies to original records without treating copied reports as independent verification. Method version 0.2.0. Establish truth, independence, credibility percentages or agreement from publication count.

CORE-005 input dictionary
Input	Unit / type	Required interpretation
nodes — Origin and report nodes	records / json	Each node has id, label, kind (origin/report), derivesFrom IDs, optional observedAt ISO timestamp and optional contested flag. Origins require originStatus (traceable/unknown). Traceable originals may share an originClusterId when documentation shows they represent the same observation.

Visible mathematics / decision rules

origins(report) = union of reachable declared original records

known origin clusters = distinct declared originClusterId values plus each ungrouped traceable origin record; reports with unknown origins are counted separately

earliest traceable origin time = minimum supplied original timestamp, only when every reachable traceable origin has a timestamp; otherwise unknown

What this result cannot establish

A declared origin is not an independently verified origin. Different IDs may describe dependent records.
The graph exposes declared relationships only; an empty report source list means unknown origin.

What would falsify the assumptions?

Supply an original record contradicting a dependency or identifying a previously unknown origin.
Demonstrate that two declared origins share the same original observation.
A copied false report can produce more publications without improving evidence.
Assumptions fail if the supplied definitions, units, period, scope or source records are wrong or incomplete.
An independent source audit or replication may contradict an input or the recorded dependency.
This method creates no resources. Physical conservation is checked in the inventory and transfer ledgers; descriptive graphs and checklists are not resource-flow models.
The same equations and review checklist can be reproduced with a spreadsheet or structured human-only process.
This is deterministic arithmetic, a descriptive comparison or a structured governance check, not a fitted causal effect or forecast. Fictional inputs remain an exercise.
Equal or worse measured errors, net review effort, disclosure burden or fallback under MAR would show no added benefit or added risk; no improvement is assumed.

Reproducible fictional fixture and independently calculated expectations

This fixture is synthetic. It does not describe a country, government, company or event.

{
  "observations": {
    "unit": "records",
    "origin": "synthetic",
    "sources": [
      {
        "id": "SYN-001",
        "kind": "synthetic",
        "label": "Fictional duplicate reports",
        "url": null
      }
    ],
    "nodes": [
      {
        "id": "O1",
        "label": "Original observation one",
        "kind": "origin",
        "originStatus": "traceable",
        "derivesFrom": []
      },
      {
        "id": "O2",
        "label": "Original observation two",
        "kind": "origin",
        "originStatus": "traceable",
        "derivesFrom": []
      },
      {
        "id": "A",
        "label": "Article A",
        "kind": "report",
        "derivesFrom": [
          "O1"
        ]
      },
      {
        "id": "B",
        "label": "Article B",
        "kind": "report",
        "derivesFrom": [
          "O1"
        ]
      },
      {
        "id": "C",
        "label": "Article C",
        "kind": "report",
        "derivesFrom": [
          "O1"
        ]
      },
      {
        "id": "D",
        "label": "Article D",
        "kind": "report",
        "derivesFrom": [
          "O2"
        ]
      },
      {
        "id": "E",
        "label": "Article E",
        "kind": "report",
        "derivesFrom": []
      }
    ]
  },
  "parameters": {}
}

Expected checks

{
  "knownOriginClusters": 2,
  "unknownOriginReports": 1,
  "reportCount": 5
}

Download input fixture

Authoritative background references

NIST: AI Risk Management Framework — Voluntary governance background; not validation of MAR. Reviewed 2026-11-09. Reference, not endorsement or validation.

These sources inform definitions or review practices. They do not validate this implementation or endorse MAR.

Open the original Scenario Studio Examine original hypotheses Find related policy modules