{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:2X3C3MHUGMWXMBLDSNPW64AJYB","short_pith_number":"pith:2X3C3MHU","canonical_record":{"source":{"id":"2602.01099","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2026-02-01T08:46:45Z","cross_cats_sorted":["cs.NA","math.NA"],"title_canon_sha256":"ae3ab79e6f06fd4df93f3d2720718bfb3e227e1090366031cb81ee4175a96b36","abstract_canon_sha256":"592accfab5b48fac937ee9132f9bf36af9a3dcc98b112f41e92a16e767d585ec"},"schema_version":"1.0"},"canonical_sha256":"d5f62db0f4332d760563935f6f7009c050001bf3b359e64d4ed29cf3fd8e5c51","source":{"kind":"arxiv","id":"2602.01099","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2602.01099","created_at":"2026-05-18T03:09:24Z"},{"alias_kind":"arxiv_version","alias_value":"2602.01099v2","created_at":"2026-05-18T03:09:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2602.01099","created_at":"2026-05-18T03:09:24Z"},{"alias_kind":"pith_short_12","alias_value":"2X3C3MHUGMWX","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"2X3C3MHUGMWXMBLD","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"2X3C3MHU","created_at":"2026-05-18T12:33:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:2X3C3MHUGMWXMBLDSNPW64AJYB","target":"record","payload":{"canonical_record":{"source":{"id":"2602.01099","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2026-02-01T08:46:45Z","cross_cats_sorted":["cs.NA","math.NA"],"title_canon_sha256":"ae3ab79e6f06fd4df93f3d2720718bfb3e227e1090366031cb81ee4175a96b36","abstract_canon_sha256":"592accfab5b48fac937ee9132f9bf36af9a3dcc98b112f41e92a16e767d585ec"},"schema_version":"1.0"},"canonical_sha256":"d5f62db0f4332d760563935f6f7009c050001bf3b359e64d4ed29cf3fd8e5c51","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:09:24.047066Z","signature_b64":"WnQMV88CPeLgt9kUw0t7xO+2Z1aJa0PuKv09XuOJ6nXcX+BbPLkqQotT1vI3RgzALAUzWoMHe2VfX6WIlKLcDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d5f62db0f4332d760563935f6f7009c050001bf3b359e64d4ed29cf3fd8e5c51","last_reissued_at":"2026-05-18T03:09:24.046148Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:09:24.046148Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2602.01099","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T03:09:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gv2/vQ15RSUjfePypXALhmcxSoBy80f9oAj724LVW3VItfFEmvTIuaAODfV9ZVsQ+6j7pQJ1t0AW1IlE2MOhAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T19:03:11.113654Z"},"content_sha256":"25ca056f00524f6aa4f9726cbb1a846f7e0f8f7126e24e923bf0e7ef5d43ff6d","schema_version":"1.0","event_id":"sha256:25ca056f00524f6aa4f9726cbb1a846f7e0f8f7126e24e923bf0e7ef5d43ff6d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:2X3C3MHUGMWXMBLDSNPW64AJYB","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Simultaneous Estimation of Seabed and Its Roughness With Longitudinal Waves","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"An infinite-dimensional Bayesian method uses statistical isotropy and fractional differentiability to simultaneously estimate seabed topography and roughness from acoustic wave scattering.","cross_cats":["cs.NA","math.NA"],"primary_cat":"stat.AP","authors_text":"Ana Carpio, Babak Maboudi Afkham","submitted_at":"2026-02-01T08:46:45Z","abstract_excerpt":"This paper introduces an infinite-dimensional Bayesian framework for acoustic seabed tomography, leveraging wave scattering to simultaneously estimate the seabed and its roughness. Tomography is considered an ill-posed problem where multiple seabed configurations can result in similar measurement patterns. We propose a novel approach focusing on the statistical isotropy of the seabed. Utilizing fractional differentiability to identify seabed roughness, the paper presents a robust numerical algorithm to estimate the seabed and quantify uncertainties. Extensive numerical experiments validate the"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"The paper presents a robust numerical algorithm to estimate the seabed and quantify uncertainties using an infinite-dimensional Bayesian framework leveraging wave scattering and fractional differentiability under statistical isotropy.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The seabed exhibits statistical isotropy, allowing fractional differentiability to identify roughness; this assumption is central to making the ill-posed tomography problem tractable.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"An infinite-dimensional Bayesian framework estimates seabed topography and roughness simultaneously from acoustic data by assuming statistical isotropy and using fractional differentiability.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"An infinite-dimensional Bayesian method uses statistical isotropy and fractional differentiability to simultaneously estimate seabed topography and roughness from acoustic wave scattering.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"2abfa24304e919e32e81dc4559236c47487b67cec12e537f8574638c4108689e"},"source":{"id":"2602.01099","kind":"arxiv","version":2},"verdict":{"id":"54dfcd8a-c221-488d-bea4-bda77a141f27","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-16T08:47:50.985233Z","strongest_claim":"The paper presents a robust numerical algorithm to estimate the seabed and quantify uncertainties using an infinite-dimensional Bayesian framework leveraging wave scattering and fractional differentiability under statistical isotropy.","one_line_summary":"An infinite-dimensional Bayesian framework estimates seabed topography and roughness simultaneously from acoustic data by assuming statistical isotropy and using fractional differentiability.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The seabed exhibits statistical isotropy, allowing fractional differentiability to identify roughness; this assumption is central to making the ill-posed tomography problem tractable.","pith_extraction_headline":"An infinite-dimensional Bayesian method uses statistical isotropy and fractional differentiability to simultaneously estimate seabed topography and roughness from acoustic wave scattering."},"references":{"count":22,"sample":[{"doi":"10.1016/j.amc.2025.129453","year":2025,"title":"[1]C. Abugattas, A. Carpio, E. Cebri ´an, and G. Oleaga,Quantifying uncertainty in inverse scattering problems set in layered environments, Applied Mathematics and Computation, 500 (2025), p. 129453, ","work_id":"e2712637-ea44-4e2a-96da-6f22f6c1873a","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"10.1007/s10851-024-01207-9","year":2024,"title":"ESTIMATION OF SEABED AND ROUGHNESS WITH LONGITUDINAL WAVES29 [4]B. M. Afkham, K. Knudsen, A. K. Rasmussen, and T. Tarvainen,A bayesian approach for consistent reconstruction of inclusions, Inverse Pro","work_id":"c80676bc-672b-4a2c-8ac6-b18c889ae5eb","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"10.1121/10.0037871","year":2025,"title":"[9]J. Bonnel, A. Vardi, J. Leonard, and S. Dosso,From geoacoustic inversion to seabed tomog- raphy using a distributed network of sources and receivers, The Journal of the Acoustical Society of Americ","work_id":"2d701af5-f78a-458e-87ab-c2094203afdc","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"10.1088/1361-6420/acd5f8","year":2023,"title":"[15]A. Carpio, E. Cebri ´an, and A. Guti ´errez,Object based Bayesian full-waveform inversion for shear elastography, Inverse Problems, 39 (2023), p. 075007, https://doi.org/10.1088/ 1361-6420/acd5f8,","work_id":"f67c9091-2a2b-41d1-91ee-3c39fec220ae","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"10.1007/978-3-319-12385-1","year":2017,"title":"In: Handbook of Uncertainty Quantification, pp","work_id":"04b71a60-724d-4ea5-987b-89e39872fbe9","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":22,"snapshot_sha256":"43fdb30bc46f3d854a71a4cb4a504cb9a7a910157b8c09715bd3868698dcf9ef","internal_anchors":2},"formal_canon":{"evidence_count":2,"snapshot_sha256":"39681e9ddb54536a1c07f82776b1ce9c11fca1e92d80d5bf652423a364291add"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":"54dfcd8a-c221-488d-bea4-bda77a141f27"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T03:09:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LB2Y8O92plBuZLmAFE54Fyc/2VtvQiDUs5PDhEzpLLopFlNuo6UVVo4d7w97LZQ0+iAE685G33TPDHEPsK4WCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T19:03:11.114783Z"},"content_sha256":"07f9752f4453c83a3e7ad6e566411732d48d4fda9a5ce36d6744cba223ed1fb4","schema_version":"1.0","event_id":"sha256:07f9752f4453c83a3e7ad6e566411732d48d4fda9a5ce36d6744cba223ed1fb4"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/2X3C3MHUGMWXMBLDSNPW64AJYB/bundle.json","state_url":"https://pith.science/pith/2X3C3MHUGMWXMBLDSNPW64AJYB/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/2X3C3MHUGMWXMBLDSNPW64AJYB/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-25T19:03:11Z","links":{"resolver":"https://pith.science/pith/2X3C3MHUGMWXMBLDSNPW64AJYB","bundle":"https://pith.science/pith/2X3C3MHUGMWXMBLDSNPW64AJYB/bundle.json","state":"https://pith.science/pith/2X3C3MHUGMWXMBLDSNPW64AJYB/state.json","well_known_bundle":"https://pith.science/.well-known/pith/2X3C3MHUGMWXMBLDSNPW64AJYB/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:2X3C3MHUGMWXMBLDSNPW64AJYB","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"592accfab5b48fac937ee9132f9bf36af9a3dcc98b112f41e92a16e767d585ec","cross_cats_sorted":["cs.NA","math.NA"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2026-02-01T08:46:45Z","title_canon_sha256":"ae3ab79e6f06fd4df93f3d2720718bfb3e227e1090366031cb81ee4175a96b36"},"schema_version":"1.0","source":{"id":"2602.01099","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2602.01099","created_at":"2026-05-18T03:09:24Z"},{"alias_kind":"arxiv_version","alias_value":"2602.01099v2","created_at":"2026-05-18T03:09:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2602.01099","created_at":"2026-05-18T03:09:24Z"},{"alias_kind":"pith_short_12","alias_value":"2X3C3MHUGMWX","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"2X3C3MHUGMWXMBLD","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"2X3C3MHU","created_at":"2026-05-18T12:33:37Z"}],"graph_snapshots":[{"event_id":"sha256:07f9752f4453c83a3e7ad6e566411732d48d4fda9a5ce36d6744cba223ed1fb4","target":"graph","created_at":"2026-05-18T03:09:24Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"The paper presents a robust numerical algorithm to estimate the seabed and quantify uncertainties using an infinite-dimensional Bayesian framework leveraging wave scattering and fractional differentiability under statistical isotropy."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"The seabed exhibits statistical isotropy, allowing fractional differentiability to identify roughness; this assumption is central to making the ill-posed tomography problem tractable."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"An infinite-dimensional Bayesian framework estimates seabed topography and roughness simultaneously from acoustic data by assuming statistical isotropy and using fractional differentiability."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"An infinite-dimensional Bayesian method uses statistical isotropy and fractional differentiability to simultaneously estimate seabed topography and roughness from acoustic wave scattering."}],"snapshot_sha256":"2abfa24304e919e32e81dc4559236c47487b67cec12e537f8574638c4108689e"},"formal_canon":{"evidence_count":2,"snapshot_sha256":"39681e9ddb54536a1c07f82776b1ce9c11fca1e92d80d5bf652423a364291add"},"paper":{"abstract_excerpt":"This paper introduces an infinite-dimensional Bayesian framework for acoustic seabed tomography, leveraging wave scattering to simultaneously estimate the seabed and its roughness. Tomography is considered an ill-posed problem where multiple seabed configurations can result in similar measurement patterns. We propose a novel approach focusing on the statistical isotropy of the seabed. Utilizing fractional differentiability to identify seabed roughness, the paper presents a robust numerical algorithm to estimate the seabed and quantify uncertainties. Extensive numerical experiments validate the","authors_text":"Ana Carpio, Babak Maboudi Afkham","cross_cats":["cs.NA","math.NA"],"headline":"An infinite-dimensional Bayesian method uses statistical isotropy and fractional differentiability to simultaneously estimate seabed topography and roughness from acoustic wave scattering.","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2026-02-01T08:46:45Z","title":"Simultaneous Estimation of Seabed and Its Roughness With Longitudinal Waves"},"references":{"count":22,"internal_anchors":2,"resolved_work":22,"sample":[{"cited_arxiv_id":"","doi":"10.1016/j.amc.2025.129453","is_internal_anchor":false,"ref_index":1,"title":"[1]C. Abugattas, A. Carpio, E. Cebri ´an, and G. Oleaga,Quantifying uncertainty in inverse scattering problems set in layered environments, Applied Mathematics and Computation, 500 (2025), p. 129453, ","work_id":"e2712637-ea44-4e2a-96da-6f22f6c1873a","year":2025},{"cited_arxiv_id":"","doi":"10.1007/s10851-024-01207-9","is_internal_anchor":false,"ref_index":2,"title":"ESTIMATION OF SEABED AND ROUGHNESS WITH LONGITUDINAL WAVES29 [4]B. M. Afkham, K. Knudsen, A. K. Rasmussen, and T. Tarvainen,A bayesian approach for consistent reconstruction of inclusions, Inverse Pro","work_id":"c80676bc-672b-4a2c-8ac6-b18c889ae5eb","year":2024},{"cited_arxiv_id":"","doi":"10.1121/10.0037871","is_internal_anchor":false,"ref_index":3,"title":"[9]J. Bonnel, A. Vardi, J. Leonard, and S. Dosso,From geoacoustic inversion to seabed tomog- raphy using a distributed network of sources and receivers, The Journal of the Acoustical Society of Americ","work_id":"2d701af5-f78a-458e-87ab-c2094203afdc","year":2025},{"cited_arxiv_id":"","doi":"10.1088/1361-6420/acd5f8","is_internal_anchor":false,"ref_index":4,"title":"[15]A. Carpio, E. Cebri ´an, and A. Guti ´errez,Object based Bayesian full-waveform inversion for shear elastography, Inverse Problems, 39 (2023), p. 075007, https://doi.org/10.1088/ 1361-6420/acd5f8,","work_id":"f67c9091-2a2b-41d1-91ee-3c39fec220ae","year":2023},{"cited_arxiv_id":"","doi":"10.1007/978-3-319-12385-1","is_internal_anchor":false,"ref_index":5,"title":"In: Handbook of Uncertainty Quantification, pp","work_id":"04b71a60-724d-4ea5-987b-89e39872fbe9","year":2017}],"snapshot_sha256":"43fdb30bc46f3d854a71a4cb4a504cb9a7a910157b8c09715bd3868698dcf9ef"},"source":{"id":"2602.01099","kind":"arxiv","version":2},"verdict":{"created_at":"2026-05-16T08:47:50.985233Z","id":"54dfcd8a-c221-488d-bea4-bda77a141f27","model_set":{"reader":"grok-4.3"},"one_line_summary":"An infinite-dimensional Bayesian framework estimates seabed topography and roughness simultaneously from acoustic data by assuming statistical isotropy and using fractional differentiability.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"An infinite-dimensional Bayesian method uses statistical isotropy and fractional differentiability to simultaneously estimate seabed topography and roughness from acoustic wave scattering.","strongest_claim":"The paper presents a robust numerical algorithm to estimate the seabed and quantify uncertainties using an infinite-dimensional Bayesian framework leveraging wave scattering and fractional differentiability under statistical isotropy.","weakest_assumption":"The seabed exhibits statistical isotropy, allowing fractional differentiability to identify roughness; this assumption is central to making the ill-posed tomography problem tractable."}},"verdict_id":"54dfcd8a-c221-488d-bea4-bda77a141f27"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:25ca056f00524f6aa4f9726cbb1a846f7e0f8f7126e24e923bf0e7ef5d43ff6d","target":"record","created_at":"2026-05-18T03:09:24Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"592accfab5b48fac937ee9132f9bf36af9a3dcc98b112f41e92a16e767d585ec","cross_cats_sorted":["cs.NA","math.NA"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2026-02-01T08:46:45Z","title_canon_sha256":"ae3ab79e6f06fd4df93f3d2720718bfb3e227e1090366031cb81ee4175a96b36"},"schema_version":"1.0","source":{"id":"2602.01099","kind":"arxiv","version":2}},"canonical_sha256":"d5f62db0f4332d760563935f6f7009c050001bf3b359e64d4ed29cf3fd8e5c51","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d5f62db0f4332d760563935f6f7009c050001bf3b359e64d4ed29cf3fd8e5c51","first_computed_at":"2026-05-18T03:09:24.046148Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T03:09:24.046148Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"WnQMV88CPeLgt9kUw0t7xO+2Z1aJa0PuKv09XuOJ6nXcX+BbPLkqQotT1vI3RgzALAUzWoMHe2VfX6WIlKLcDA==","signature_status":"signed_v1","signed_at":"2026-05-18T03:09:24.047066Z","signed_message":"canonical_sha256_bytes"},"source_id":"2602.01099","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:25ca056f00524f6aa4f9726cbb1a846f7e0f8f7126e24e923bf0e7ef5d43ff6d","sha256:07f9752f4453c83a3e7ad6e566411732d48d4fda9a5ce36d6744cba223ed1fb4"],"state_sha256":"535c03d33d5d68095c3e26164ed361ec56a186969a87dca29d9439e5211131cf"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gwsK80QtH21XaAHFXT2nRiEi1K4gi5BHXmd+clA77+1W4GYZLXYr4BqJHu1QGGYtrIQDxkVZ0tVdquJkheGXDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T19:03:11.121099Z","bundle_sha256":"c3e64f916b3e86c4e483888defa0c3448ce985cec2e5d79806e9fec46f45768e"}}