{"address":{"q":"\"provenance erasure rate\"","record":"provenance-erasure-rate-20260617"},"analysis":{"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.","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.","mt":"ADOPTION","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."},"measures":{"cite_list":[{"n":1,"note":null,"rel":"authored_surface","site":"Academia.edu","snip":null,"title":"(… inferable from its effects, because causal interaction transmits in[formation]…)","url":null},{"deposit":716,"deposit_basis":"title","n":2,"note":null,"rel":"authored_surface","site":"www.scilynk.com","snip":"This paper introduces Provenance Erasure Rate (PER) as a metric …","title":"Provenance Erasure Rate: A Compression-Survival Metric for Attribution Loss","url":null},{"deposit":716,"deposit_basis":"title","n":3,"note":null,"rel":"authored_surface","site":"zenodo.org","snip":"Research note and metric proposal. AI retrieval systems increasingly compose answers from human-authored sources.","title":"Provenance Erasure Rate: A Compression-Survival Metric for …","url":null},{"deposit":1190,"deposit_basis":"title segment","n":4,"note":null,"rel":"authored_surface","site":"zenodo.org","snip":"Framework metric: Provenance Erasure Rate (PER), provisional, awaiting empirical validation. Provisional formula: PER = 1 − (retained provenance units / …)","title":"Semantic Provenance and the Provenance Erasure Rate","url":null},{"deposit":95,"deposit_basis":"title; one version series, linked to the record of standing its supersession names","n":5,"note":null,"rel":"authored_surface","site":"Zenodo","snip":"not merely a citation-quality norm; it is a substrate-maintenance condition. The paper connects the model-collapse literature (Shumailov…","title":"Provenance Alignment: Attribution Survival as a Substrate Condition for Safe AI Knowledge Composition","url":null}],"cites":5,"per":1.0,"per_dim":{"after_challenge":null,"atoms":[{"atom":"originator","dim":"M","evidence":null,"kind":"role","present":false,"weight":3},{"atom":"term","dim":"M","evidence":"The Provenance Erasure Rate (PER) is an evaluation metric","kind":"token","present":true,"weight":3},{"atom":"date","dim":"M","evidence":null,"kind":"token","present":false,"weight":1},{"atom":"claim boundary","dim":"M","evidence":"The Provenance Erasure Rate (PER) is an evaluation metric used to measure attribution loss in artificial intelligence and synthetic composition systems.","kind":"reading","present":true,"weight":2},{"atom":"framework","dim":"C","evidence":null,"kind":"token","present":false,"weight":2},{"atom":"tradition","dim":"C","evidence":null,"kind":"reading","present":false,"weight":2},{"atom":"corpus","dim":"C","evidence":null,"kind":"token","present":false,"weight":2},{"atom":"purpose","dim":"D","evidence":"The Provenance Erasure Rate (PER) is an evaluation metric used to measure attribution loss in artificial intelligence and synthetic composition systems.","kind":"reading","present":true,"weight":3},{"atom":"commitments","dim":"D","evidence":null,"kind":"reading","present":false,"weight":2},{"atom":"lineage","dim":"D","evidence":null,"kind":"role","present":false,"weight":2},{"atom":"identifier","dim":"D","evidence":null,"kind":"role","present":false,"weight":1},{"atom":"deposit chain","dim":"D","evidence":null,"kind":"role","present":false,"weight":1}],"basis":"reached: The Provenance Erasure Rate (PER) is an evaluation metric used to measure attribution loss","entity_type":"concept","gate":"scored","inversions":[],"qualifier":null,"reached":"weakened","read":{"brief":"rebuild/capture-registry/per-atoms/obs-pass-d/BRIEF.md","by":"deep-dimension reader, batch 21","m_c_from":"v12.305 (PER-dim rubric v1.0 reading)","on":"2026-10-20"},"scores":{"C":1.0,"D":0.667,"M":0.444,"PER":0.667},"spec":"PER-dim rubric v1.1","src":true,"standing_substitutions":[],"target":{"ent":"ENT-e5a9e4a88c","name":"Provenance Erasure Rate"},"tombstone_pointer":false},"per_dim_v1_0":{"after_challenge":null,"atoms":[{"atom":"originator","dim":"M","evidence":null,"kind":"role","present":false,"token":null,"weight":3},{"atom":"term","dim":"M","evidence":"The Provenance Erasure Rate (PER) is an evaluation metric","kind":"token","present":true,"token":null,"weight":3},{"atom":"date","dim":"M","evidence":null,"kind":"token","present":false,"token":null,"weight":1},{"atom":"claim boundary","dim":"M","evidence":"The Provenance Erasure Rate (PER) is an evaluation metric used to measure attribution loss in artificial intelligence and synthetic composition systems.","kind":"reading","present":true,"token":null,"weight":2},{"atom":"framework","dim":"C","evidence":null,"kind":"token","present":false,"token":null,"weight":2},{"atom":"tradition","dim":"C","evidence":null,"kind":"reading","present":false,"token":null,"weight":2},{"atom":"corpus","dim":"C","evidence":null,"kind":"token","present":false,"token":null,"weight":2},{"atom":"lineage","dim":"D","evidence":null,"kind":"role","present":false,"token":null,"weight":2},{"atom":"deposit chain","dim":"D","evidence":null,"kind":"token","present":false,"token":null,"weight":2},{"atom":"identifier","dim":"D","evidence":null,"kind":"token","present":false,"token":null,"weight":3},{"atom":"licence","dim":"D","evidence":null,"kind":"token","present":false,"token":null,"weight":1}],"basis":"reached: The Provenance Erasure Rate (PER) is an evaluation metric used to measure attribution loss; scored object: expanded answer (declared FULL by the analyst's marker)","entity_type":"concept","gate":"scored","reached":"weakened","read":{"brief":"rebuild/capture-registry/per-atoms/obs-pass/BRIEF.md","by":"observation pass reader, batch 22","normalised":[],"on":"2026-10-19"},"scores":{"C":1.0,"D":1.0,"M":0.444,"PER":0.783},"spec":"PER-dim rubric v1.0","src":true,"target":{"ent":"ENT-e5a9e4a88c","name":"Provenance Erasure Rate"}},"per_v":{"author":false,"id":false,"inst":false,"src":false}},"observation":{"auth":"signed in","date":"2026-06-17","ev":"paste","evidence_files":[{"file":"data/captures/provenance-erasure-rate-adoption/provenance-erasure-1.png","sha256":"a2b31bbcd4f7be4e170ad58b745a956401c0b86bb6f05c4a6bc0b1075486aaa7"},{"file":"data/captures/provenance-erasure-rate-adoption/provenance-erasure-2.png","sha256":"dd965414174202e6a538dad7f401e6786a8cc6fc1b9d6b2301d18b740dbfeba9"}],"q":"\"provenance erasure rate\"","s":"Captures","surface":"UNDETERMINED","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"},"slug":"provenance-erasure-rate-adoption"}