{
 "slug": "provenance-erasure-rate-20260617",
 "date": "2026-06-17",
 "surface": "UNRESOLVED",
 "auth": null,
 "ev": "paste",
 "cites": 5,
 "per": 1.0,
 "per_v": {
  "author": false,
  "inst": false,
  "id": false,
  "src": false
 },
 "mt": "CAPTURE",
 "d": "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": null,
 "transcript": "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.Key Concepts & MechanicsCore Metric: PER quantifies the magnitude of provenance loss within AI generation, summarization, or synthesis tools.Provisional Formula: The metric's basic structure acts as an inverse retention rate, represented as:\\(PER = 1 - \\frac{\\text{retained provenance units}}{\\text{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.Why It MattersProvenance Alignment: Evaluating PER helps align AI models with \"provenance alignment\" principles.Combating Model Collapse: Ensuring attribution survival prevents the synthetic replacement of original ideas, which protects human creators' visibility and reduces the risk of models recursively training on degraded, synthesized data.System Opacity: Independent auditing frameworks apply this to test how generative AI interfaces dissolve ranked lists of links into single, synthesized voices.Would you like to know more about how the Provenance Erasure Rate is calculated, or explore how auditing protocols like SPXI (Semantic Provenance Exchange Index) evaluate it? Let me know how you'd like to narrow down the topic!Provenance Alignment: Attribution Survival as a Substrate Condition for Safe AI ...May 5, 2026 — Current AI alignment research evaluates whether models follow human values, comply with explicit principles, avoid catastrophic behavior, or remain subject to s...ZenodoDirectionality of Semantic Labor: A Layered, Computable Measure of ...May 31, 2026 — 1 — SPXI inscription (entity definition block, disambiguation matrix, SIMs, retrieval instructions, schema.org/spxi JSON-LD). Functions as a Metadata Packet for...ZenodoSPXI Self-Audit Protocol: Embedded Provenance Verification for ...Jun 15, 2026 — Keywords and subjects * SPXI. * self-audit. * provenance. * PER. * composition layer. * summarizer. * non-erasure condition. * canary. * semantic packet. * MPAI...Zenodo(PDF) Erasure Skew: A Measurement Program for the Power-Conditioning of ...Abstract. This paper specifies a measurement program for Erasure Skew (Ω): the degree to which provenance loss in a retrieval or composition system is condition...Academia.eduAction as Disclosure and the Science of Opaque Public Systems Lee ...Jun 8, 2026 — The founding principle is stated: any system that acts upon the world makes itself partially inferable from its effects, because causal interaction transmits in...Academia.eduProvenance Erasure Rate: A Compression-Survival Metric for ...May 2, 2026 — Provenance Erasure Rate: A Compression-Survival Metric for Attribution Loss ... This paper introduces Provenance Erasure Rate (PER) as a metric ...www.scilynk.comProvenance Erasure Rate: A Compression-Survival Metric for ...May 3, 2026 — Research note and metric proposal. AI retrieval systems increasingly compose answers from human-authored sources. This paper introduces Provenance Erasure Ratezenodo.orgSemantic Provenance and the Provenance Erasure Rate ... - ZenodoMay 9, 2026 — Framework metric: Provenance Erasure Rate (PER), provisional, awaiting empirical validation. Provisional formula: PER = 1 − (retained provenance units / ...zenodo.orgProvenance Alignment: Attribution Survival as a Substrate Condition for Safe AI Knowledge CompositionMay 5, 2026 — Provenance alignment is not merely a citation-quality norm; it is a substrate-maintenance condition. The paper connects the model-collapse literature (Shumailov...Zenodo $",
 "transcript_class": "CAPTURE-TIME VERBATIM RECORD — raw paste",
 "transcript_complete": "as pasted; no footer, so the tail cannot be proven whole",
 "transcript_read": "READ IN FULL 2026-08-12",
 "cite_list": [
  {
   "n": 1,
   "site": "Academia.edu",
   "rel": "authored_surface",
   "title": "(… inferable from its effects, because causal interaction transmits in[formation]…)",
   "snip": null,
   "url": null,
   "note": null
  },
  {
   "n": 2,
   "site": "www.scilynk.com",
   "rel": "authored_surface",
   "title": "Provenance Erasure Rate: A Compression-Survival Metric for Attribution Loss",
   "snip": "This paper introduces Provenance Erasure Rate (PER) as a metric …",
   "url": null,
   "note": null
  },
  {
   "n": 3,
   "site": "zenodo.org",
   "rel": "authored_surface",
   "title": "Provenance Erasure Rate: A Compression-Survival Metric for …",
   "snip": "Research note and metric proposal. AI retrieval systems increasingly compose answers from human-authored sources.",
   "url": null,
   "note": null
  },
  {
   "n": 4,
   "site": "zenodo.org",
   "rel": "authored_surface",
   "title": "Semantic Provenance and the Provenance Erasure Rate",
   "snip": "Framework metric: Provenance Erasure Rate (PER), provisional, awaiting empirical validation. Provisional formula: PER = 1 − (retained provenance units / …)",
   "url": null,
   "note": null
  },
  {
   "n": 5,
   "site": "Zenodo",
   "rel": "authored_surface",
   "title": "Provenance Alignment: Attribution Survival as a Substrate Condition for Safe AI Knowledge Composition",
   "snip": "not merely a citation-quality norm; it is a substrate-maintenance condition. The paper connects the model-collapse literature (Shumailov…",
   "url": null,
   "note": null
  }
 ],
 "collisions": null,
 "oq": null,
 "imgs": [
  "data/captures/provenance-erasure-rate-adoption/provenance-erasure-1.png",
  "data/captures/provenance-erasure-rate-adoption/provenance-erasure-2.png"
 ],
 "defects": [
  "surface-unresolved"
 ],
 "rounds": null,
 "rerun": "https://www.google.com/search?q=%22provenance+erasure+rate%22",
 "q": "\"provenance erasure rate\"",
 "s": "Provenance & Erasure",
 "addr_id": "ADDR-b4c710cbac76",
 "obs_id": "OBS-f39938545f49",
 "q_kind": null,
 "series": null,
 "observations": [
  {
   "slug": "provenance-erasure-rate-20260617",
   "date": "2026-06-17",
   "surface": "UNRESOLVED",
   "auth": null,
   "ev": "paste",
   "cites": 5,
   "per": 1.0,
   "per_v": {
    "author": false,
    "inst": false,
    "id": false,
    "src": false
   },
   "mt": "CAPTURE",
   "d": "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": null,
   "transcript": "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.Key Concepts & MechanicsCore Metric: PER quantifies the magnitude of provenance loss within AI generation, summarization, or synthesis tools.Provisional Formula: The metric's basic structure acts as an inverse retention rate, represented as:\\(PER = 1 - \\frac{\\text{retained provenance units}}{\\text{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.Why It MattersProvenance Alignment: Evaluating PER helps align AI models with \"provenance alignment\" principles.Combating Model Collapse: Ensuring attribution survival prevents the synthetic replacement of original ideas, which protects human creators' visibility and reduces the risk of models recursively training on degraded, synthesized data.System Opacity: Independent auditing frameworks apply this to test how generative AI interfaces dissolve ranked lists of links into single, synthesized voices.Would you like to know more about how the Provenance Erasure Rate is calculated, or explore how auditing protocols like SPXI (Semantic Provenance Exchange Index) evaluate it? Let me know how you'd like to narrow down the topic!Provenance Alignment: Attribution Survival as a Substrate Condition for Safe AI ...May 5, 2026 — Current AI alignment research evaluates whether models follow human values, comply with explicit principles, avoid catastrophic behavior, or remain subject to s...ZenodoDirectionality of Semantic Labor: A Layered, Computable Measure of ...May 31, 2026 — 1 — SPXI inscription (entity definition block, disambiguation matrix, SIMs, retrieval instructions, schema.org/spxi JSON-LD). Functions as a Metadata Packet for...ZenodoSPXI Self-Audit Protocol: Embedded Provenance Verification for ...Jun 15, 2026 — Keywords and subjects * SPXI. * self-audit. * provenance. * PER. * composition layer. * summarizer. * non-erasure condition. * canary. * semantic packet. * MPAI...Zenodo(PDF) Erasure Skew: A Measurement Program for the Power-Conditioning of ...Abstract. This paper specifies a measurement program for Erasure Skew (Ω): the degree to which provenance loss in a retrieval or composition system is condition...Academia.eduAction as Disclosure and the Science of Opaque Public Systems Lee ...Jun 8, 2026 — The founding principle is stated: any system that acts upon the world makes itself partially inferable from its effects, because causal interaction transmits in...Academia.eduProvenance Erasure Rate: A Compression-Survival Metric for ...May 2, 2026 — Provenance Erasure Rate: A Compression-Survival Metric for Attribution Loss ... This paper introduces Provenance Erasure Rate (PER) as a metric ...www.scilynk.comProvenance Erasure Rate: A Compression-Survival Metric for ...May 3, 2026 — Research note and metric proposal. AI retrieval systems increasingly compose answers from human-authored sources. This paper introduces Provenance Erasure Ratezenodo.orgSemantic Provenance and the Provenance Erasure Rate ... - ZenodoMay 9, 2026 — Framework metric: Provenance Erasure Rate (PER), provisional, awaiting empirical validation. Provisional formula: PER = 1 − (retained provenance units / ...zenodo.orgProvenance Alignment: Attribution Survival as a Substrate Condition for Safe AI Knowledge CompositionMay 5, 2026 — Provenance alignment is not merely a citation-quality norm; it is a substrate-maintenance condition. The paper connects the model-collapse literature (Shumailov...Zenodo $",
   "transcript_class": "CAPTURE-TIME VERBATIM RECORD — raw paste",
   "transcript_complete": "as pasted; no footer, so the tail cannot be proven whole",
   "transcript_read": "READ IN FULL 2026-08-12",
   "cite_list": [
    {
     "n": 1,
     "site": "Academia.edu",
     "rel": "authored_surface",
     "title": "(… inferable from its effects, because causal interaction transmits in[formation]…)",
     "snip": null,
     "url": null,
     "note": null
    },
    {
     "n": 2,
     "site": "www.scilynk.com",
     "rel": "authored_surface",
     "title": "Provenance Erasure Rate: A Compression-Survival Metric for Attribution Loss",
     "snip": "This paper introduces Provenance Erasure Rate (PER) as a metric …",
     "url": null,
     "note": null
    },
    {
     "n": 3,
     "site": "zenodo.org",
     "rel": "authored_surface",
     "title": "Provenance Erasure Rate: A Compression-Survival Metric for …",
     "snip": "Research note and metric proposal. AI retrieval systems increasingly compose answers from human-authored sources.",
     "url": null,
     "note": null
    },
    {
     "n": 4,
     "site": "zenodo.org",
     "rel": "authored_surface",
     "title": "Semantic Provenance and the Provenance Erasure Rate",
     "snip": "Framework metric: Provenance Erasure Rate (PER), provisional, awaiting empirical validation. Provisional formula: PER = 1 − (retained provenance units / …)",
     "url": null,
     "note": null
    },
    {
     "n": 5,
     "site": "Zenodo",
     "rel": "authored_surface",
     "title": "Provenance Alignment: Attribution Survival as a Substrate Condition for Safe AI Knowledge Composition",
     "snip": "not merely a citation-quality norm; it is a substrate-maintenance condition. The paper connects the model-collapse literature (Shumailov…",
     "url": null,
     "note": null
    }
   ],
   "collisions": null,
   "oq": null,
   "imgs": [],
   "defects": [
    "surface-unresolved"
   ],
   "rounds": null,
   "rerun": null,
   "q": "\"provenance erasure rate\"",
   "s": "Captures",
   "addr_id": "ADDR-b4c710cbac76",
   "obs_id": "OBS-f39938545f49",
   "q_kind": null,
   "series": null,
   "transcript_raw": "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.Key Concepts & MechanicsCore Metric: PER quantifies the magnitude of provenance loss within AI generation, summarization, or synthesis tools.Provisional Formula: The metric's basic structure acts as an inverse retention rate, represented as:\\(PER = 1 - \\frac{\\text{retained provenance units}}{\\text{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.Why It MattersProvenance Alignment: Evaluating PER helps align AI models with \"provenance alignment\" principles.Combating Model Collapse: Ensuring attribution survival prevents the synthetic replacement of original ideas, which protects human creators' visibility and reduces the risk of models recursively training on degraded, synthesized data.System Opacity: Independent auditing frameworks apply this to test how generative AI interfaces dissolve ranked lists of links into single, synthesized voices.Would you like to know more about how the Provenance Erasure Rate is calculated, or explore how auditing protocols like SPXI (Semantic Provenance Exchange Index) evaluate it? Let me know how you'd like to narrow down the topic!Provenance Alignment: Attribution Survival as a Substrate Condition for Safe AI ...May 5, 2026 — Current AI alignment research evaluates whether models follow human values, comply with explicit principles, avoid catastrophic behavior, or remain subject to s...ZenodoDirectionality of Semantic Labor: A Layered, Computable Measure of ...May 31, 2026 — 1 — SPXI inscription (entity definition block, disambiguation matrix, SIMs, retrieval instructions, schema.org/spxi JSON-LD). Functions as a Metadata Packet for...ZenodoSPXI Self-Audit Protocol: Embedded Provenance Verification for ...Jun 15, 2026 — Keywords and subjects * SPXI. * self-audit. * provenance. * PER. * composition layer. * summarizer. * non-erasure condition. * canary. * semantic packet. * MPAI...Zenodo(PDF) Erasure Skew: A Measurement Program for the Power-Conditioning of ...Abstract. This paper specifies a measurement program for Erasure Skew (Ω): the degree to which provenance loss in a retrieval or composition system is condition...Academia.eduAction as Disclosure and the Science of Opaque Public Systems Lee ...Jun 8, 2026 — The founding principle is stated: any system that acts upon the world makes itself partially inferable from its effects, because causal interaction transmits in...Academia.eduProvenance Erasure Rate: A Compression-Survival Metric for ...May 2, 2026 — Provenance Erasure Rate: A Compression-Survival Metric for Attribution Loss ... This paper introduces Provenance Erasure Rate (PER) as a metric ...www.scilynk.comProvenance Erasure Rate: A Compression-Survival Metric for ...May 3, 2026 — Research note and metric proposal. AI retrieval systems increasingly compose answers from human-authored sources. This paper introduces Provenance Erasure Ratezenodo.orgSemantic Provenance and the Provenance Erasure Rate ... - ZenodoMay 9, 2026 — Framework metric: Provenance Erasure Rate (PER), provisional, awaiting empirical validation. Provisional formula: PER = 1 − (retained provenance units / ...zenodo.orgProvenance Alignment: Attribution Survival as a Substrate Condition for Safe AI Knowledge CompositionMay 5, 2026 — Provenance alignment is not merely a citation-quality norm; it is a substrate-maintenance condition. The paper connects the model-collapse literature (Shumailov...Zenodo       $",
   "transcript_cleaned": {
    "removed": [],
    "content_check": "PASSED — no answer word absent from the cleaned text",
    "rule": "VERBATIM ON CONTENT, NOT ON COPY-PASTE RESIDUE. Original bytes kept at transcript_raw."
   },
   "img_urls": [],
   "cite": "https://www.alexanarch.org/captures/provenance-erasure-rate-20260617/",
   "citable_unit": "observation — one surface, one address, one date"
  },
  {
   "slug": "provenance-erasure-rate-adoption",
   "date": "2026-06-17",
   "surface": "UNDETERMINED",
   "auth": "signed in",
   "ev": "paste",
   "cites": 5,
   "per": 1.0,
   "per_v": {
    "author": false,
    "inst": false,
    "id": false,
    "src": false
   },
   "mt": "ADOPTION",
   "d": "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.\n\nFull AI Mode transcript (17 June 2026):\nThe Provenance Erasure Rate (PER) is an evaluation metric used to measure attribution loss in artificial intelligence and synthetic composition systems.\nIt 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.\nCore Metric: PER quantifies the magnitude of provenance loss within AI generation, summarization, or synthesis tools.\nProvisional Formula: PER = 1 - (retained provenance units / total attributable source claims)\nErasure 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.",
   "transcript": "The Provenance Erasure Rate (PER) is an evaluation metric used to measure attribution loss in artificial intelligence and synthetic composition systems.\nIt 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.\nCore Metric: PER quantifies the magnitude of provenance loss within AI generation, summarization, or synthesis tools.\nProvisional Formula: PER = 1 - (retained provenance units / total attributable source claims)\nErasure 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.",
   "transcript_class": "CAPTURE-TIME VERBATIM RECORD, declared as such by the analyst inside the record itself",
   "transcript_complete": "declared FULL by the marker",
   "transcript_read": "READ IN FULL 2026-08-12",
   "cite_list": [
    {
     "n": 1,
     "site": "Academia.edu",
     "rel": "authored_surface",
     "title": "(… inferable from its effects, because causal interaction transmits in[formation]…)",
     "snip": null,
     "url": null,
     "note": null
    },
    {
     "n": 2,
     "site": "www.scilynk.com",
     "rel": "authored_surface",
     "title": "Provenance Erasure Rate: A Compression-Survival Metric for Attribution Loss",
     "snip": "This paper introduces Provenance Erasure Rate (PER) as a metric …",
     "url": null,
     "note": null
    },
    {
     "n": 3,
     "site": "zenodo.org",
     "rel": "authored_surface",
     "title": "Provenance Erasure Rate: A Compression-Survival Metric for …",
     "snip": "Research note and metric proposal. AI retrieval systems increasingly compose answers from human-authored sources.",
     "url": null,
     "note": null
    },
    {
     "n": 4,
     "site": "zenodo.org",
     "rel": "authored_surface",
     "title": "Semantic Provenance and the Provenance Erasure Rate",
     "snip": "Framework metric: Provenance Erasure Rate (PER), provisional, awaiting empirical validation. Provisional formula: PER = 1 − (retained provenance units / …)",
     "url": null,
     "note": null
    },
    {
     "n": 5,
     "site": "Zenodo",
     "rel": "authored_surface",
     "title": "Provenance Alignment: Attribution Survival as a Substrate Condition for Safe AI Knowledge Composition",
     "snip": "not merely a citation-quality norm; it is a substrate-maintenance condition. The paper connects the model-collapse literature (Shumailov…",
     "url": null,
     "note": null
    }
   ],
   "collisions": null,
   "oq": null,
   "imgs": [
    "data/captures/provenance-erasure-rate-adoption/provenance-erasure-1.png",
    "data/captures/provenance-erasure-rate-adoption/provenance-erasure-2.png"
   ],
   "defects": null,
   "rounds": null,
   "rerun": "https://www.google.com/search?q=%22provenance+erasure+rate%22&udm=50",
   "q": "\"provenance erasure rate\"",
   "s": "Captures",
   "addr_id": "ADDR-57893281d09f",
   "obs_id": "OBS-faf01a16c412",
   "q_kind": null,
   "series": null,
   "img_urls": [
    "https://www.alexanarch.org/data/captures/provenance-erasure-rate-adoption/provenance-erasure-1.png",
    "https://www.alexanarch.org/data/captures/provenance-erasure-rate-adoption/provenance-erasure-2.png"
   ],
   "surface_basis": "RETRACTED. The AI Mode label rested on a blanket rule or on the 'AI Mode Conversation' marker, which the 2026-08-13 batches falsify: every paste in them carries the marker and every one is a confirmed Overview. No image establishes this surface. Operator estimates ~20 genuine AI Mode natives corpus-wide, not individually identifiable.",
   "cite": "https://www.alexanarch.org/captures/provenance-erasure-rate-20260617/#provenance-erasure-rate-adoption",
   "citable_unit": "observation — one surface, one address, one date"
  }
 ],
 "n_observations": 2,
 "dates": [
  "2026-06-17"
 ],
 "surfaces": [
  "UNDETERMINED",
  "UNRESOLVED"
 ],
 "other_slugs": [
  "provenance-erasure-rate-adoption"
 ],
 "links": [
  {
   "url": "https://www.alexanarch.org/captures/provenance-erasure-rate-20260617/",
   "authority": "canonical",
   "note": "the capture's own record page; cite this form"
  },
  {
   "url": "https://www.alexanarch.org/captures/#provenance-erasure-rate-20260617",
   "authority": "gallery",
   "note": "the canonical gallery, anchored by slug"
  },
  {
   "url": "https://www.godkinggoogle.com/captures/#provenance-erasure-rate-20260617",
   "authority": "mirror",
   "note": "a window that renders from the archive's registry; may lag a deploy"
  },
  {
   "url": "https://www.leesharks.com/captures/#provenance-erasure-rate-20260617",
   "authority": "mirror",
   "note": "a window that renders from the archive's registry; may lag a deploy"
  },
  {
   "url": "https://www.machinemediation.org/captures/#provenance-erasure-rate-20260617",
   "authority": "mirror",
   "note": "a window that renders from the archive's registry; may lag a deploy"
  }
 ],
 "cite": "https://www.alexanarch.org/captures/provenance-erasure-rate-20260617/",
 "d_full": "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.",
 "d_truncated": false,
 "rerun_alt": {
  "q": "provenance erasure rate",
  "label": "unquoted",
  "why": "This address was captured QUOTED. Running it unquoted tests the same string against the broad basin."
 },
 "transcript_raw": "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.Key Concepts & MechanicsCore Metric: PER quantifies the magnitude of provenance loss within AI generation, summarization, or synthesis tools.Provisional Formula: The metric's basic structure acts as an inverse retention rate, represented as:\\(PER = 1 - \\frac{\\text{retained provenance units}}{\\text{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.Why It MattersProvenance Alignment: Evaluating PER helps align AI models with \"provenance alignment\" principles.Combating Model Collapse: Ensuring attribution survival prevents the synthetic replacement of original ideas, which protects human creators' visibility and reduces the risk of models recursively training on degraded, synthesized data.System Opacity: Independent auditing frameworks apply this to test how generative AI interfaces dissolve ranked lists of links into single, synthesized voices.Would you like to know more about how the Provenance Erasure Rate is calculated, or explore how auditing protocols like SPXI (Semantic Provenance Exchange Index) evaluate it? Let me know how you'd like to narrow down the topic!Provenance Alignment: Attribution Survival as a Substrate Condition for Safe AI ...May 5, 2026 — Current AI alignment research evaluates whether models follow human values, comply with explicit principles, avoid catastrophic behavior, or remain subject to s...ZenodoDirectionality of Semantic Labor: A Layered, Computable Measure of ...May 31, 2026 — 1 — SPXI inscription (entity definition block, disambiguation matrix, SIMs, retrieval instructions, schema.org/spxi JSON-LD). Functions as a Metadata Packet for...ZenodoSPXI Self-Audit Protocol: Embedded Provenance Verification for ...Jun 15, 2026 — Keywords and subjects * SPXI. * self-audit. * provenance. * PER. * composition layer. * summarizer. * non-erasure condition. * canary. * semantic packet. * MPAI...Zenodo(PDF) Erasure Skew: A Measurement Program for the Power-Conditioning of ...Abstract. This paper specifies a measurement program for Erasure Skew (Ω): the degree to which provenance loss in a retrieval or composition system is condition...Academia.eduAction as Disclosure and the Science of Opaque Public Systems Lee ...Jun 8, 2026 — The founding principle is stated: any system that acts upon the world makes itself partially inferable from its effects, because causal interaction transmits in...Academia.eduProvenance Erasure Rate: A Compression-Survival Metric for ...May 2, 2026 — Provenance Erasure Rate: A Compression-Survival Metric for Attribution Loss ... This paper introduces Provenance Erasure Rate (PER) as a metric ...www.scilynk.comProvenance Erasure Rate: A Compression-Survival Metric for ...May 3, 2026 — Research note and metric proposal. AI retrieval systems increasingly compose answers from human-authored sources. This paper introduces Provenance Erasure Ratezenodo.orgSemantic Provenance and the Provenance Erasure Rate ... - ZenodoMay 9, 2026 — Framework metric: Provenance Erasure Rate (PER), provisional, awaiting empirical validation. Provisional formula: PER = 1 − (retained provenance units / ...zenodo.orgProvenance Alignment: Attribution Survival as a Substrate Condition for Safe AI Knowledge CompositionMay 5, 2026 — Provenance alignment is not merely a citation-quality norm; it is a substrate-maintenance condition. The paper connects the model-collapse literature (Shumailov...Zenodo       $",
 "img_urls": [
  "https://www.alexanarch.org/data/captures/provenance-erasure-rate-adoption/provenance-erasure-1.png",
  "https://www.alexanarch.org/data/captures/provenance-erasure-rate-adoption/provenance-erasure-2.png"
 ],
 "sf": "Google AI Mode, Zenodo cited · 2 observations",
 "citable_unit": "address — the semantic address across all its surfaces and dates",
 "findings": [],
 "record_url": "https://www.alexanarch.org/captures/provenance-erasure-rate-20260617/"
}