{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:OJUEAO3YFFR72Z465Z3IGQF4KR","short_pith_number":"pith:OJUEAO3Y","canonical_record":{"source":{"id":"2511.15743","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-11-18T20:13:25Z","cross_cats_sorted":["astro-ph.EP","astro-ph.IM"],"title_canon_sha256":"48757c29fbc0e4a38b5cf59e20f7e26e69164970cc5d8a07f2fdf1b5c3c788ca","abstract_canon_sha256":"80682f963035a586a94a0c6b5c693f1a3198f5492176de3d94dc90732b764efd"},"schema_version":"1.0"},"canonical_sha256":"7268403b782963fd679eee768340bc54507060804c3cffcb68691b06d9dcbb89","source":{"kind":"arxiv","id":"2511.15743","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2511.15743","created_at":"2026-05-18T03:09:33Z"},{"alias_kind":"arxiv_version","alias_value":"2511.15743v2","created_at":"2026-05-18T03:09:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2511.15743","created_at":"2026-05-18T03:09:33Z"},{"alias_kind":"pith_short_12","alias_value":"OJUEAO3YFFR7","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"OJUEAO3YFFR72Z46","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"OJUEAO3Y","created_at":"2026-05-18T12:33:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:OJUEAO3YFFR72Z465Z3IGQF4KR","target":"record","payload":{"canonical_record":{"source":{"id":"2511.15743","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-11-18T20:13:25Z","cross_cats_sorted":["astro-ph.EP","astro-ph.IM"],"title_canon_sha256":"48757c29fbc0e4a38b5cf59e20f7e26e69164970cc5d8a07f2fdf1b5c3c788ca","abstract_canon_sha256":"80682f963035a586a94a0c6b5c693f1a3198f5492176de3d94dc90732b764efd"},"schema_version":"1.0"},"canonical_sha256":"7268403b782963fd679eee768340bc54507060804c3cffcb68691b06d9dcbb89","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:09:33.179886Z","signature_b64":"Z9Aq1ePFgWRNhcYwKkrca5JCPsZoxGGVlwWheMEaAzrILfOwsr2d4pMZ8mYwX5cCxK8Is+Eh66nx98UuDrZ9Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7268403b782963fd679eee768340bc54507060804c3cffcb68691b06d9dcbb89","last_reissued_at":"2026-05-18T03:09:33.179367Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:09:33.179367Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2511.15743","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":"2026-05-18T03:09:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8WidrnIiEQIYmCPe2CrPP2X5IBY5K5uq/uPGnnzkHp7Rl3jcUGm3vJoT2HegInw2ztPesti8AUYtCuV6iCZ5Cw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T12:47:54.389346Z"},"content_sha256":"6978283d2d3d69a134393e51c5ab64b0aca8f860365f9c2cad60d06c24064fdf","schema_version":"1.0","event_id":"sha256:6978283d2d3d69a134393e51c5ab64b0aca8f860365f9c2cad60d06c24064fdf"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:OJUEAO3YFFR72Z465Z3IGQF4KR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Connecting the Dots: A Machine Learning Ready Dataset for Ionospheric Forecasting Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"A curated open dataset integrates solar, geomagnetic and ionospheric measurements into a single machine-learning-ready structure for forecasting total electron content.","cross_cats":["astro-ph.EP","astro-ph.IM"],"primary_cat":"cs.LG","authors_text":"At{\\i}l{\\i}m G\\\"une\\c{s} Baydin, Bala Poduval, Frank Soboczenski, Giacomo Acciarini, Halil S. Kelebek, Linnea M. Wolniewicz, Madhulika Guhathakurta, Michael D. Vergalla, Olga Verkhoglyadova, Simone Mestici, Thomas E. Berger","submitted_at":"2025-11-18T20:13:25Z","abstract_excerpt":"Operational forecasting of the ionosphere remains a critical space weather challenge due to sparse observations, complex coupling across geospatial layers, and a growing need for timely, accurate predictions that support Global Navigation Satellite System (GNSS), communications, aviation safety, as well as satellite operations. As part of the 2025 NASA Heliolab, we present a curated, open-access dataset that integrates diverse ionospheric and heliospheric measurements into a coherent, machine learning-ready structure, designed specifically to support next-generation forecasting models and addr"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"We present a curated, open-access dataset that integrates diverse ionospheric and heliospheric measurements into a coherent, machine learning-ready structure, designed specifically to support next-generation forecasting models.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The assumption that the chosen data sources can be temporally and spatially aligned without introducing systematic biases that would degrade downstream ML model performance, particularly for the crowdsourced smartphone TEC measurements.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"The authors release a heterogeneous, temporally and spatially aligned dataset combining solar, geomagnetic, and ionospheric sources and benchmark spatiotemporal ML models for vertical TEC forecasting.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"A curated open dataset integrates solar, geomagnetic and ionospheric measurements into a single machine-learning-ready structure for forecasting total electron content.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"ab4b4a1eb996ef01c424fe7ddd735d2ee965f50aa3c4607e94350c24ccdbe358"},"source":{"id":"2511.15743","kind":"arxiv","version":2},"verdict":{"id":"23d475af-0f8d-4281-8496-6d04c0914c3a","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-17T20:13:22.290102Z","strongest_claim":"We present a curated, open-access dataset that integrates diverse ionospheric and heliospheric measurements into a coherent, machine learning-ready structure, designed specifically to support next-generation forecasting models.","one_line_summary":"The authors release a heterogeneous, temporally and spatially aligned dataset combining solar, geomagnetic, and ionospheric sources and benchmark spatiotemporal ML models for vertical TEC forecasting.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The assumption that the chosen data sources can be temporally and spatially aligned without introducing systematic biases that would degrade downstream ML model performance, particularly for the crowdsourced smartphone TEC measurements.","pith_extraction_headline":"A curated open dataset integrates solar, geomagnetic and ionospheric measurements into a single machine-learning-ready structure for forecasting total electron content."},"references":{"count":25,"sample":[{"doi":"","year":2020,"title":"Flying through uncertainty.Space Weather, 18(1):e2019SW002373, 2020","work_id":"979ddb61-26b0-40ee-87f9-98862c263e43","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":1976,"title":"Jr. Kintner, P. M. Observations of velocity shear driven plasma turbulence.Journal of Geophys- ical Research, 81(A28):5114–5122, October 1976","work_id":"68a9fbb4-bb9b-4b0d-abb3-259ddd8b02cc","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2022,"title":"Unexpected space weather causing the reentry of 38 starlink satellites in february 2022.Journal of Space Weather and Space Climate, 12:41, 2022","work_id":"f87e4b86-79c7-4f4b-9b2f-a8a9da577e6d","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2017,"title":"Geomagnetically induced currents: Science, engineering, and applications readiness.Space weather, 15(7):828–856, 2017","work_id":"83ced498-be9a-4545-a354-6351a6ec4f89","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2025,"title":"The tandem reconnection and cusp electrodynamics reconnaissance satellites (tracers) mission design.Space science reviews, 221(5):1–23, 2025","work_id":"92ac4ad2-d5c7-4319-beaf-fdf3471363ed","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":25,"snapshot_sha256":"ae2c008d6a33f231457e02d0812e52bbff957d368b10fb398bc3ffb08292be8f","internal_anchors":0},"formal_canon":{"evidence_count":2,"snapshot_sha256":"6605a909d0ed3714c47efba1bae43bb61a923726e5e08d1c0dd94b1883be8f7a"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":"23d475af-0f8d-4281-8496-6d04c0914c3a"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T03:09:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"4IczonTh7cUvSnDIBbzZGQEvJVKTbrmJxBhg8DVkaJ4cLzSoE0uvqALSRrSK9p60WtIuDVpCo9AFCdwJ3luZDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T12:47:54.390007Z"},"content_sha256":"ae95eee1daab8cc2922fda48b1c1f4905de02d62f34de2bb43017c23ed2cc020","schema_version":"1.0","event_id":"sha256:ae95eee1daab8cc2922fda48b1c1f4905de02d62f34de2bb43017c23ed2cc020"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/OJUEAO3YFFR72Z465Z3IGQF4KR/bundle.json","state_url":"https://pith.science/pith/OJUEAO3YFFR72Z465Z3IGQF4KR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/OJUEAO3YFFR72Z465Z3IGQF4KR/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":"2026-06-02T12:47:54Z","links":{"resolver":"https://pith.science/pith/OJUEAO3YFFR72Z465Z3IGQF4KR","bundle":"https://pith.science/pith/OJUEAO3YFFR72Z465Z3IGQF4KR/bundle.json","state":"https://pith.science/pith/OJUEAO3YFFR72Z465Z3IGQF4KR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/OJUEAO3YFFR72Z465Z3IGQF4KR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:OJUEAO3YFFR72Z465Z3IGQF4KR","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":"80682f963035a586a94a0c6b5c693f1a3198f5492176de3d94dc90732b764efd","cross_cats_sorted":["astro-ph.EP","astro-ph.IM"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-11-18T20:13:25Z","title_canon_sha256":"48757c29fbc0e4a38b5cf59e20f7e26e69164970cc5d8a07f2fdf1b5c3c788ca"},"schema_version":"1.0","source":{"id":"2511.15743","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2511.15743","created_at":"2026-05-18T03:09:33Z"},{"alias_kind":"arxiv_version","alias_value":"2511.15743v2","created_at":"2026-05-18T03:09:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2511.15743","created_at":"2026-05-18T03:09:33Z"},{"alias_kind":"pith_short_12","alias_value":"OJUEAO3YFFR7","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"OJUEAO3YFFR72Z46","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"OJUEAO3Y","created_at":"2026-05-18T12:33:37Z"}],"graph_snapshots":[{"event_id":"sha256:ae95eee1daab8cc2922fda48b1c1f4905de02d62f34de2bb43017c23ed2cc020","target":"graph","created_at":"2026-05-18T03:09:33Z","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":"We present a curated, open-access dataset that integrates diverse ionospheric and heliospheric measurements into a coherent, machine learning-ready structure, designed specifically to support next-generation forecasting models."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"The assumption that the chosen data sources can be temporally and spatially aligned without introducing systematic biases that would degrade downstream ML model performance, particularly for the crowdsourced smartphone TEC measurements."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"The authors release a heterogeneous, temporally and spatially aligned dataset combining solar, geomagnetic, and ionospheric sources and benchmark spatiotemporal ML models for vertical TEC forecasting."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"A curated open dataset integrates solar, geomagnetic and ionospheric measurements into a single machine-learning-ready structure for forecasting total electron content."}],"snapshot_sha256":"ab4b4a1eb996ef01c424fe7ddd735d2ee965f50aa3c4607e94350c24ccdbe358"},"formal_canon":{"evidence_count":2,"snapshot_sha256":"6605a909d0ed3714c47efba1bae43bb61a923726e5e08d1c0dd94b1883be8f7a"},"paper":{"abstract_excerpt":"Operational forecasting of the ionosphere remains a critical space weather challenge due to sparse observations, complex coupling across geospatial layers, and a growing need for timely, accurate predictions that support Global Navigation Satellite System (GNSS), communications, aviation safety, as well as satellite operations. As part of the 2025 NASA Heliolab, we present a curated, open-access dataset that integrates diverse ionospheric and heliospheric measurements into a coherent, machine learning-ready structure, designed specifically to support next-generation forecasting models and addr","authors_text":"At{\\i}l{\\i}m G\\\"une\\c{s} Baydin, Bala Poduval, Frank Soboczenski, Giacomo Acciarini, Halil S. Kelebek, Linnea M. Wolniewicz, Madhulika Guhathakurta, Michael D. Vergalla, Olga Verkhoglyadova, Simone Mestici, Thomas E. Berger","cross_cats":["astro-ph.EP","astro-ph.IM"],"headline":"A curated open dataset integrates solar, geomagnetic and ionospheric measurements into a single machine-learning-ready structure for forecasting total electron content.","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-11-18T20:13:25Z","title":"Connecting the Dots: A Machine Learning Ready Dataset for Ionospheric Forecasting Models"},"references":{"count":25,"internal_anchors":0,"resolved_work":25,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"Flying through uncertainty.Space Weather, 18(1):e2019SW002373, 2020","work_id":"979ddb61-26b0-40ee-87f9-98862c263e43","year":2020},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"Jr. Kintner, P. M. Observations of velocity shear driven plasma turbulence.Journal of Geophys- ical Research, 81(A28):5114–5122, October 1976","work_id":"68a9fbb4-bb9b-4b0d-abb3-259ddd8b02cc","year":1976},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"Unexpected space weather causing the reentry of 38 starlink satellites in february 2022.Journal of Space Weather and Space Climate, 12:41, 2022","work_id":"f87e4b86-79c7-4f4b-9b2f-a8a9da577e6d","year":2022},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":4,"title":"Geomagnetically induced currents: Science, engineering, and applications readiness.Space weather, 15(7):828–856, 2017","work_id":"83ced498-be9a-4545-a354-6351a6ec4f89","year":2017},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":5,"title":"The tandem reconnection and cusp electrodynamics reconnaissance satellites (tracers) mission design.Space science reviews, 221(5):1–23, 2025","work_id":"92ac4ad2-d5c7-4319-beaf-fdf3471363ed","year":2025}],"snapshot_sha256":"ae2c008d6a33f231457e02d0812e52bbff957d368b10fb398bc3ffb08292be8f"},"source":{"id":"2511.15743","kind":"arxiv","version":2},"verdict":{"created_at":"2026-05-17T20:13:22.290102Z","id":"23d475af-0f8d-4281-8496-6d04c0914c3a","model_set":{"reader":"grok-4.3"},"one_line_summary":"The authors release a heterogeneous, temporally and spatially aligned dataset combining solar, geomagnetic, and ionospheric sources and benchmark spatiotemporal ML models for vertical TEC forecasting.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"A curated open dataset integrates solar, geomagnetic and ionospheric measurements into a single machine-learning-ready structure for forecasting total electron content.","strongest_claim":"We present a curated, open-access dataset that integrates diverse ionospheric and heliospheric measurements into a coherent, machine learning-ready structure, designed specifically to support next-generation forecasting models.","weakest_assumption":"The assumption that the chosen data sources can be temporally and spatially aligned without introducing systematic biases that would degrade downstream ML model performance, particularly for the crowdsourced smartphone TEC measurements."}},"verdict_id":"23d475af-0f8d-4281-8496-6d04c0914c3a"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:6978283d2d3d69a134393e51c5ab64b0aca8f860365f9c2cad60d06c24064fdf","target":"record","created_at":"2026-05-18T03:09:33Z","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":"80682f963035a586a94a0c6b5c693f1a3198f5492176de3d94dc90732b764efd","cross_cats_sorted":["astro-ph.EP","astro-ph.IM"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-11-18T20:13:25Z","title_canon_sha256":"48757c29fbc0e4a38b5cf59e20f7e26e69164970cc5d8a07f2fdf1b5c3c788ca"},"schema_version":"1.0","source":{"id":"2511.15743","kind":"arxiv","version":2}},"canonical_sha256":"7268403b782963fd679eee768340bc54507060804c3cffcb68691b06d9dcbb89","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7268403b782963fd679eee768340bc54507060804c3cffcb68691b06d9dcbb89","first_computed_at":"2026-05-18T03:09:33.179367Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T03:09:33.179367Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Z9Aq1ePFgWRNhcYwKkrca5JCPsZoxGGVlwWheMEaAzrILfOwsr2d4pMZ8mYwX5cCxK8Is+Eh66nx98UuDrZ9Dg==","signature_status":"signed_v1","signed_at":"2026-05-18T03:09:33.179886Z","signed_message":"canonical_sha256_bytes"},"source_id":"2511.15743","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6978283d2d3d69a134393e51c5ab64b0aca8f860365f9c2cad60d06c24064fdf","sha256:ae95eee1daab8cc2922fda48b1c1f4905de02d62f34de2bb43017c23ed2cc020"],"state_sha256":"a6e05e1db036c67d4955b944bf3623407274f915376fa9986b8190f24c5eed08"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"uEsxZas7cvEsAQAv5MoMyOsvVP0rEKWM1ARRbGgA/KVghse5I+RagXJcYFNlgTp3pK7rrox9WbnORBV5YRqoCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-02T12:47:54.392626Z","bundle_sha256":"1d7debe0c0c9b6d691853832f974fff248ac5b033c4c981e5d8accca7862e9a8"}}