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A Contribution Fingerprint (CF) is like a receipt with provenance for one piece of work. Whenever someone submits a sample, label, or validation (we call these atomic contributions), the system creates one CF that permanently links:
  • What was contributed (a stable reference to the content),
  • Who did it (the contributor’s wallet or decentralized ID),
  • When it happened (a timestamp/anchor),
  • Why it’s credible (attached evidence and review signals).
CFs are the first brick of the whole architecture. They make authorship clear, power transparent ownership and royalties, and let anyone trace a dataset back to its origins.

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Where CFs fit (big picture)

  1. Contribution → one CF per atomic contribution.
  2. Assetification → many CFs are assembled into data assets and versioned datasets.
  3. Usage → AI builders access datasets via policy‑gated endpoints; usage is metered.
  4. Royalty → revenue + metered events + ownership fractions = deterministic payouts.

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How a CF is created

Step explanations
  • Submit — The contributor sends the content. We save an encrypted copy and a stable reference (content ID or URL).
  • Pre‑scan — Light checks for format, privacy redactions, and duplicates. Some items pass automatically; others may need a quick human review.
  • Create the record — We standardize the details, mark whether it’s a sample, label, or validation, and attach evidence & signals (see below).
  • Bind identity — The contributor approves/signs the record using a wallet or decentralized ID (DID).
  • Anchor on chain — A fingerprint is immutably anchored to a blockchain (individually or in a batch).
  • Publish — The CF becomes discoverable. Access to the original content is policy‑gated.
  • Evaluate & amend — Reviewers add their own CFs (evaluations) or file disputes. We never overwrite the original; updates create new CFs that reference earlier ones, preserving the full history.

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Evidence & signals (how we back a claim)

To keep trust high without exposing private data, each CF can carry structured evidence and signals:
  • Evidence (artifacts): transaction IDs, document or image hashes, stable links, or signed credentials that support the claim.
  • Signals: how the claim was obtained (source class such as heuristic, ML inference, primary document, or on‑chain ground truth) and how it was checked (validation class such as none, peer‑reviewed, staked verification, or adjudicated). We can also record agreement levels and reviewer reputations.
Reviewers and algorithms do not edit the original CF. They append their own CFs that reference it. That keeps the audit trail clean and explainable.

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What CFs enable

  • Clear authorship — Every atomic contribution has one CF at birth; credit is never lost.
  • Traceable datasets — Any dataset can be walked back through assets to the CFs that built it.
  • Provable ownership — CFs anchor tokenized, transferable ownership fractions.
  • Fair payouts — When data is used, metered events and ownership fractions point back to the right CFs, enabling deterministic royalties.
  • Accountable improvements — Validations and corrections are new CFs instead of silent edits.

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Example: from contribution to royalties

Dr. Lin contributes a label for a medical image, which was provided by the patient from a recent pathology diagnosis. The image upload creates one CF (for the sample); Dr. Lin’s label creates a second CF linked to the first. Both are anchored on-chain and published (the content stays policy-gated). Independent reviewers add their own CFs to confirm or challenge the label; disagreements open a dispute that results in a new CF (amend/deprecate) without editing history. These CFs are assembled into a versioned dataset. Tokenized ownership fractions are recorded for the patient (sample), Dr. Lin (label), validators (reviews), any backers, and the treasury. An AI builder accesses the dataset via the access gateway; usage is metered, payments flow to the builder’s revenue account, and the royalty engine combines revenue, usage events, and time-pinned

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Invariants (always true)

  • Append-only CFs: One CF per atomic contribution at creation; any later change leads to a creation of new CF, which is linked to the original (e.g., corrected label, added evidence, validator decision, privacy redaction). Views point to the latest valid CF; earlier CFs remain immutable and auditable.
  • Deterministic & auditable: the same inputs reproduce the same CF and the same payouts.
  • Traceable: served data can be traced from dataset → asset → CF → contributor.
  • Minimal disclosure: sensitive payloads remain encrypted; access is policy‑gated.