{"address":{"q":"erasure skew","record":"erasure-skew-1"},"analysis":{"analysis":"Multi-field synthesis. 'Erasure skew does not refer to a single, universal concept, but rather has specific technical meanings depending on the field. It generally describes an uneven, biased, or angled loss of data, parity, or representation.' Four sections: 1. Data Storage & Erasure Coding (ACM Digital Library +4), 2. Information Science & AI Provenance — 'Erasure Skew (or the Erasure Skew Coefficient) measures the orientation of that data loss. It evaluates who the data loss falls upon, tracking whether the erasure is fair and proportionate or if it disproportionately affects specific narratives, topics, or communities' (Catalyst California +2), 3. Magnetic Recording (IEEE +3), 4. Quantum Computing. The archive's provenance-measurement concept sits alongside ACM, IEEE, and Catalyst California as a co-equal field definition.","d":"Multi-field synthesis. 'Erasure skew does not refer to a single, universal concept, but rather has specific technical meanings depending on the field. It generally describes an uneven, biased, or angled loss of data, parity, or representation.' Four sections: 1. Data Storage & Erasure Coding (ACM Digital Library +4), 2. Information Science & AI Provenance — 'Erasure Skew (or the Erasure Skew Coefficient) measures the orientation of that data loss. It evaluates who the data loss falls upon, tracking whether the erasure is fair and proportionate or if it disproportionately affects specific narratives, topics, or communities' (Catalyst California +2), 3. Magnetic Recording (IEEE +3), 4. Quantum Computing. The archive's provenance-measurement concept sits alongside ACM, IEEE, and Catalyst California as a co-equal field definition.","d_full":"Multi-field synthesis. 'Erasure skew does not refer to a single, universal concept, but rather has specific technical meanings depending on the field. It generally describes an uneven, biased, or angled loss of data, parity, or representation.' Four sections: 1. Data Storage & Erasure Coding (ACM Digital Library +4), 2. Information Science & AI Provenance — 'Erasure Skew (or the Erasure Skew Coefficient) measures the orientation of that data loss. It evaluates who the data loss falls upon, tracking whether the erasure is fair and proportionate or if it disproportionately affects specific narratives, topics, or communities' (Catalyst California +2), 3. Magnetic Recording (IEEE +3), 4. Quantum Computing. The archive's provenance-measurement concept sits alongside ACM, IEEE, and Catalyst California as a co-equal field definition.","defects":["truncated-by-interface","citations-null","analysis-without-finding"],"match":"BROAD MATCH","mt":"BROAD MATCH"},"measures":{"per_dim":{"after_challenge":null,"atoms":[{"atom":"originator","dim":"M","evidence":null,"kind":"role","present":false,"weight":3},{"atom":"term","dim":"M","evidence":"Erasure Skew (or the Erasure Skew Coefficient)","kind":"token","present":true,"weight":3},{"atom":"date","dim":"M","evidence":null,"kind":"token","present":false,"weight":1},{"atom":"claim boundary","dim":"M","evidence":null,"kind":"reading","present":false,"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":"measures the orientation of that data loss. It evaluates who the data loss falls upon, tracking whether the erasure is fair and proportionate or if it disproportionately affects specific narratives, topics, or communities.","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: “Erasure skew\" does not refer to a single, universal concept, but rather has specific technical meanings depending on the field.","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 4","m_c_from":"v12.305 (PER-dim rubric v1.0 reading)","on":"2026-10-20"},"scores":{"C":1.0,"D":0.667,"M":0.667,"PER":0.75},"spec":"PER-dim rubric v1.1","src":true,"standing_substitutions":[],"target":{"ent":"ENT-64d24a294a","name":"erasure skew"},"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":"Erasure Skew (or the Erasure Skew Coefficient)","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":null,"kind":"reading","present":false,"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: “Erasure skew\" does not refer to a single, universal concept, but rather has specific technical meanings depending on the field.; scored object: visible excerpt (OCR, chrome interleaved, truncated by 'Show more')","entity_type":"concept","gate":"scored","reached":"weakened","read":{"brief":"rebuild/capture-registry/per-atoms/obs-pass/BRIEF.md","by":"observation pass reader, batch 9","normalised":[],"on":"2026-10-19"},"scores":{"C":1.0,"D":1.0,"M":0.667,"PER":0.87},"spec":"PER-dim rubric v1.0","src":true,"target":{"ent":"ENT-64d24a294a","name":"erasure skew"}}},"observation":{"auth":"signed in","date":"2026-06-15","ev":"ocr","evidence_files":[{"file":"data/captures/erasure-skew-1/erasure-skew-search.png","sha256":"1c286a7f65f4816069355b515238c7397d64d9355c0d078eed77cce753b23516"},{"file":"data/captures/erasure-skew-1/erasure-skew-expanded.png","sha256":"646a6bf95fbeae2b57cd62e6f9db51e4ce4707d8ed11b0a20a7b92019eb00390"}],"q":"erasure skew","s":"Captures","sf":"AI Overview","surface":"Google AI Overview","transcript":"Q. erasure skew x & AlMode_- All Shopping Images Videos News + Al Overview Qo:\n\n“Erasure skew\" does not refer to a single, universal concept, but rather has specific technical meanings depending on the field. It generally describes an uneven, biased, or angled loss of data, parity, or representation.\n\n1. Data Storage & Erasure Coding\n\np Zenodo :\n\nhttps://zenodo.org ;\n\nErasure Skew: A Measurement Program for the Power- ...\n\nWhere the Provenance Erasure Rate (PER) measures the\n\nmagnitude of provenance loss, Erasure Skew measures its orientation — whether the loss falls evenly across ...\n\nPeople also ask\n\nWhat does erasure mean? Vv What is the Erasure concept? Vv 4 Zenodo -\n\nG : an uneven distribution of workload, or when a skewed data placement strategy causes hot spots, severely slowing down system throughput and the recovery/rebuilding process. Advanced systems use techniques like Elastic Reed-Solomon (ERS) codes to mitigate this. @ ACM Digital Library +4\n\n2. Information Science & Al Provenance\n\nIn algorithmic systems, when models or search\n\nengines \"forget,\" omit, or scrub information,\n\nErasure Skew (or the Erasure Skew Coefficient)\n\nmeasures the orientation of that data loss. It evaluates who the data loss falls upon, tracking whether the erasure is fair and proportionate or if it disproportionately affects specific narratives, topics, or communities. \\& Catalyst California +2\n\n3. Magnetic Recording (HDD)\n\nIn high-density hard drives and magnetic recording\n\n(such as Shingled Magnetic Recording), the Skew\n\nEffect refers to the angle of the read/write head\n\nrelative to the tracks. This angular misalignment can cause Track Squeeze and Track Erasure, where the magnetic field unintentionally overlaps and damages data on an adjacent track. « IEEE +3\n\nA Cr1antimm Camnistina","transcript_class":"OCR-READ, CHROME INTERLEAVED — NOT A PASTE. Answer text and browser furniture are mixed in one stream and have not been separated. Supports PRESENCE of wording, not RETENTION.","transcript_cleaned":{"content_check":"PASSED — no answer word absent from the cleaned text","removed":[{"n":1,"what":"interface control"},{"n":1,"what":"prompt box"}],"rule":"VERBATIM ON CONTENT, NOT ON COPY-PASTE RESIDUE. Original bytes kept at transcript_raw."},"transcript_complete":"TRUNCATED BY INTERFACE — a \"Show more\" control is in frame.","transcript_raw":"Q. erasure skew x & AlMode_- All Shopping Images Videos News + Al Overview Qo:\n\n“Erasure skew\" does not refer to a single, universal concept, but rather has specific technical meanings depending on the field. It generally describes an uneven, biased, or angled loss of data, parity, or representation.\n\n1. Data Storage & Erasure Coding\n\nShow more V\n\np Zenodo :\n\nhttps://zenodo.org ;\n\nErasure Skew: A Measurement Program for the Power- ...\n\nWhere the Provenance Erasure Rate (PER) measures the\n\nmagnitude of provenance loss, Erasure Skew measures its orientation — whether the loss falls evenly across ...\n\nPeople also ask\n\nWhat does erasure mean? Vv What is the Erasure concept? Vv 4 Zenodo -\n\nG : an uneven distribution of workload, or when a skewed data placement strategy causes hot spots, severely slowing down system throughput and the recovery/rebuilding process. Advanced systems use techniques like Elastic Reed-Solomon (ERS) codes to mitigate this. @ ACM Digital Library +4\n\n2. Information Science & Al Provenance\n\nIn algorithmic systems, when models or search\n\nengines \"forget,\" omit, or scrub information,\n\nErasure Skew (or the Erasure Skew Coefficient)\n\nmeasures the orientation of that data loss. It evaluates who the data loss falls upon, tracking whether the erasure is fair and proportionate or if it disproportionately affects specific narratives, topics, or communities. \\& Catalyst California +2\n\n3. Magnetic Recording (HDD)\n\nIn high-density hard drives and magnetic recording\n\n(such as Shingled Magnetic Recording), the Skew\n\nEffect refers to the angle of the read/write head\n\nrelative to the tracks. This angular misalignment can cause Track Squeeze and Track Erasure, where the magnetic field unintentionally overlaps and damages data on an adjacent track. « IEEE +3\n\nA Cr1antimm Camnistina\n\nAsk anything & &","transcript_read":"SEATED FROM OCR WITHOUT LINE-BY-LINE READING, 2026-08-13"},"slug":"erasure-skew-1"}