AXN:07E0.CAPTURE — "provenance erasure rate"

AXN:07E0.CAPTURE is the position of the Capture Registry address for the query “"provenance erasure rate"”. Each observation at the address (one surface, one date) is one AXN, its glyphs derived from the SHA-256 of the observation sealed with its analysis and measures.

`AXN:07E0.CAPTURE.🟡👉🌸🌱🧡🧪`

Observation: UNRESOLVED, 2026-06-17 · the capture
Description: THE SHARPEST RECORD IN THE REGISTRY. FIVE OF FIVE CARDS ARE THE ARCHIVE’S OWN PAPERS — and the composed answer carries PER 1.0: no author, no institution, no identifier, no archive source. The sources are entirely present and the attribution is entirely absent.
Reading: THE SOURCES WERE ALL THERE. This capture was already recorded as returning PER 1.0 — total provenance erasure on the archive’s own metric for provenance erasure. Reading the citations shows the full shape of it: EVERY ONE OF FIVE CARDS IS AN ARCHIVE PAPER, on SciLynk, Zenodo three times and Academia. The layer retrieved the complete source set and composed a definition naming none of it. Card 4 carries the FORMULA VERBATIM — "PER = 1 − (retained provenance units / …)" — so the instrument that measures attribution loss was displayed, in the same frame, beside an answer that lost all of it. The metric was on screen while being demonstrated. This is compositional bystanding at its limit: not one archive source among many, but every source and no attribution.
Match: EXACT MATCH — CAPTURE
PER: 1.0
Citations: 5
Sealed observation: /data/captures/sealed/provenance-erasure-rate-20260617.json — SHA-256 c3b97019ce394dbac50527cba092d61a316021e68e449e1afc50f9ce32cf6925

`AXN:07E0.CAPTURE.❌🔐🎪↗️🧬🔎`

Observation: UNDETERMINED, 2026-06-17 · the capture
Description: THE SHARPEST RECORD IN THE REGISTRY. FIVE OF FIVE CARDS ARE THE ARCHIVE’S OWN PAPERS — and the composed answer carries PER 1.0: no author, no institution, no identifier, no archive source. The sources are entirely present and the attribution is entirely absent.
Reading: THE SOURCES WERE ALL THERE. This capture was already recorded as returning PER 1.0 — total provenance erasure on the archive’s own metric for provenance erasure. Reading the citations shows the full shape of it: EVERY ONE OF FIVE CARDS IS AN ARCHIVE PAPER, on SciLynk, Zenodo three times and Academia. The layer retrieved the complete source set and composed a definition naming none of it. Card 4 carries the FORMULA VERBATIM — "PER = 1 − (retained provenance units / …)" — so the instrument that measures attribution loss was displayed, in the same frame, beside an answer that lost all of it. The metric was on screen while being demonstrated. This is compositional bystanding at its limit: not one archive source among many, but every source and no attribution.
Analysis: Full description with formula PER = 1-(retained/total). Erasure Skew. Zenodo cited. RF-058. Full AI Mode transcript (17 June 2026): The Provenance Erasure Rate (PER) is an evaluation metric used to measure attribution loss in artificial intelligence and synthetic composition systems. It calculates the fraction of human-authored or source facts that a system's retrieval or generation process dissolves, omits, or fails to properly attribute when composing an answer. Core Metric: PER quantifies the magnitude of provenance loss within AI generation, summarization, or synthesis tools. Provisional Formula: PER = 1 - (retained provenance units / total attributable source claims) Erasure Skew (Ω): A related measurement that evaluates whether attribution loss falls evenly across all sources, or systematically strips out low-power/small creators while favoring high-power sources.
Match: — ADOPTION
PER: 1.0
Citations: 5
Sealed observation: /data/captures/sealed/provenance-erasure-rate-adoption.json — SHA-256 effb85da3b581e7267200ab7275dd2202109e2c3e3b0ff5f54be3315c190a09c

Relations

Read from the fields that record them (/data/axn-relations.json); a hash names bytes and supplies no relation.

Drew on

AXN:025F.GOVERNANCE.🔥🕒⚡☉🪞💙 — Provenance Erasure Rate A Compression-Survival Metric for Attribution Loss in AI-Composed Search Outputs (card resolved at intake (title); REL-b109b1eca9ed)
AXN:04B7.UNCLASSIFIED.🕕⏳⚫🌆🎶🟡 — Provenance After AI: Semantic Provenance and the Provenance Erasure Rate as Extension of C2PA, Data Provenance (card resolved at intake (title segment); REL-c3ffd21e70cd)
AXN:026A.GOVERNANCE.🪸♆🔗📝🎇🕐 — Provenance Alignment: Attribution Survival as a Substrate Condition for Safe AI Knowledge Composition (card resolved at intake (title; one version series, linked to the record of standing its supersession names); REL-5398468c83a0)
AXN:025F.GOVERNANCE.🔥🕒⚡☉🪞💙 — Provenance Erasure Rate A Compression-Survival Metric for Attribution Loss in AI-Composed Search Outputs (card resolved at intake (title); REL-b145e17fc0e0)
AXN:04B7.UNCLASSIFIED.🕕⏳⚫🌆🎶🟡 — Provenance After AI: Semantic Provenance and the Provenance Erasure Rate as Extension of C2PA, Data Provenance (card resolved at intake (title segment); REL-4799eb1db14d)
AXN:026A.GOVERNANCE.🪸♆🔗📝🎇🕐 — Provenance Alignment: Attribution Survival as a Substrate Condition for Safe AI Knowledge Composition (card resolved at intake (title; one version series, linked to the record of standing its supersession names); REL-af86e2166d70)