{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:NNT3O7CGW4HENX4UUA62YX7FIS","short_pith_number":"pith:NNT3O7CG","canonical_record":{"source":{"id":"2411.07221","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2024-11-11T18:43:09Z","cross_cats_sorted":[],"title_canon_sha256":"0969273f21c63bd92fada3ade50ccd44c7f1f46c6d935f4b6f9e96f1e2c27b64","abstract_canon_sha256":"4dbaa8164694c12c46754bf7ca83cf38826cef0140f346aadf3a514b39b8cd37"},"schema_version":"1.0"},"canonical_sha256":"6b67b77c46b70e46df94a03dac5fe5449044ce0d618b4f1a002d626292eb9750","source":{"kind":"arxiv","id":"2411.07221","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2411.07221","created_at":"2026-06-19T16:10:29Z"},{"alias_kind":"arxiv_version","alias_value":"2411.07221v2","created_at":"2026-06-19T16:10:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.07221","created_at":"2026-06-19T16:10:29Z"},{"alias_kind":"pith_short_12","alias_value":"NNT3O7CGW4HE","created_at":"2026-06-19T16:10:29Z"},{"alias_kind":"pith_short_16","alias_value":"NNT3O7CGW4HENX4U","created_at":"2026-06-19T16:10:29Z"},{"alias_kind":"pith_short_8","alias_value":"NNT3O7CG","created_at":"2026-06-19T16:10:29Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:NNT3O7CGW4HENX4UUA62YX7FIS","target":"record","payload":{"canonical_record":{"source":{"id":"2411.07221","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2024-11-11T18:43:09Z","cross_cats_sorted":[],"title_canon_sha256":"0969273f21c63bd92fada3ade50ccd44c7f1f46c6d935f4b6f9e96f1e2c27b64","abstract_canon_sha256":"4dbaa8164694c12c46754bf7ca83cf38826cef0140f346aadf3a514b39b8cd37"},"schema_version":"1.0"},"canonical_sha256":"6b67b77c46b70e46df94a03dac5fe5449044ce0d618b4f1a002d626292eb9750","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-19T16:10:29.011196Z","signature_b64":"w345U0hspAdFEr6hOxG8Q6PaGozv4QarUJNWsJM5OJv4VHHm5ZbfO0DLRnFc6DLtA7T6Oo0T+n6VC3tZTGcnBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6b67b77c46b70e46df94a03dac5fe5449044ce0d618b4f1a002d626292eb9750","last_reissued_at":"2026-06-19T16:10:29.010764Z","signature_status":"signed_v1","first_computed_at":"2026-06-19T16:10:29.010764Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2411.07221","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":"2026-06-19T16:10:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xbi1EkJZIqt+FmtfNEfOh22bDSVwfHyz9Ft1Xl/OTz9xbfDPcVrH3VeAyOoLWz0yhOPtvLLSQPoshFh7AaPFCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-19T23:41:28.190676Z"},"content_sha256":"af7a90d85777fa4b5fbe148bf5afc873ff07460742d4c490fc0a78e0a5bb230c","schema_version":"1.0","event_id":"sha256:af7a90d85777fa4b5fbe148bf5afc873ff07460742d4c490fc0a78e0a5bb230c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:NNT3O7CGW4HENX4UUA62YX7FIS","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Self-separated and self-connected models for mediator and outcome missingness in mediation analysis","license":"http://creativecommons.org/licenses/by/4.0/","headline":"Identification of mediation effects remains possible when both mediator and outcome are missing under self-separated conditional independence or self-connected shadow variable models.","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Elizabeth A. Stuart, Fan Yang, Grace V. Ringlein, Razieh Nabi, Trang Quynh Nguyen","submitted_at":"2024-11-11T18:43:09Z","abstract_excerpt":"Missing data is a common challenge in studying treatment effects. In the context of mediation analysis, this paper addresses missingness in the mediator and outcome, focusing on identification. We first consider self-separated missingness models where identification is achieved by conditional independence assumptions. This model class is somewhat limited as it is constrained by the need to remove a certain number of connections from the model. We then turn to self-connected missingness models where identification relies on information from shadow variables. This model class turns out to contai"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"This results in templates for identification in the mediation setting, generally useful identification techniques, and perhaps most importantly a synthesis and substantial extension of shadow variable theory.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"Identification in both model classes rests on specific conditional independence assumptions (for self-separated) or the presence and properties of shadow variables (for self-connected) that are not automatically satisfied by the data and must be justified externally; the abstract states these are required for the identification results to hold.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"Introduces self-separated and self-connected missingness models for mediator and outcome missingness in mediation analysis, enabling identification via conditional independences or shadow variables and extending shadow variable theory.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Identification of mediation effects remains possible when both mediator and outcome are missing under self-separated conditional independence or self-connected shadow variable models.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"92acb8823fea37a5eb5a6deac9e6a402d036c8c3a32dd5c0b9c8ec784223682e"},"source":{"id":"2411.07221","kind":"arxiv","version":2},"verdict":{"id":"cf89d3e6-63a5-4686-9a50-a7c344398a6f","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-23T17:22:59.362603Z","strongest_claim":"This results in templates for identification in the mediation setting, generally useful identification techniques, and perhaps most importantly a synthesis and substantial extension of shadow variable theory.","one_line_summary":"Introduces self-separated and self-connected missingness models for mediator and outcome missingness in mediation analysis, enabling identification via conditional independences or shadow variables and extending shadow variable theory.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"Identification in both model classes rests on specific conditional independence assumptions (for self-separated) or the presence and properties of shadow variables (for self-connected) that are not automatically satisfied by the data and must be justified externally; the abstract states these are required for the identification results to hold.","pith_extraction_headline":"Identification of mediation effects remains possible when both mediator and outcome are missing under self-separated conditional independence or self-connected shadow variable models."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2411.07221/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":38,"sample":[{"doi":"","year":1986,"title":"The Moderator - Mediator Variable Distinction in Social Psychological Research : Conceptual , Strategic , and Statistical Considerations","work_id":"f91d87c6-1792-4d8b-958e-53abd275e013","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2021,"title":"Inference for natural mediation effects under case-cohort sampling with applications in identifying COVID -19 vaccine correlates of protection","work_id":"1e739fdb-857e-4ac9-8f66-2b7fa98f4c99","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2020,"title":"Rohit Bhattacharya, Razieh Nabi, Ilya Shpitser, and James M. Robins. Identification In Missing Data Models Represented By Directed Acyclic Graphs . In Proceedings of The 35th Uncertainty in Artificial","work_id":"7df54878-70ab-4d60-b18a-92bbb2c4690f","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"10.1097/ede.0b013e31818ef366","year":2009,"title":"Cole and Constantine E","work_id":"d5e5a75a-1343-4f69-a1ae-20f6b8c88a3f","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2024,"title":"Ghazaleh Dashti, Katherine J","work_id":"6545b2f3-1cc8-4113-915f-73b91ba38e3b","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":38,"snapshot_sha256":"6ff32f7c4a53b284bf35add646d71e67f647048f92a61171a0ac0fad43cdb1aa","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":"cf89d3e6-63a5-4686-9a50-a7c344398a6f"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-06-19T16:10:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GiCro1oPbJTFbXpUrqQl1REI7EluktcDOV4p/rwsJJ08+KFbHZF5Cjzcu0An5QkwwBHUm050CT7rFUWt+J1nAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-19T23:41:28.191231Z"},"content_sha256":"f4ecf01673cadcb0841b64296eac2fbde0dd7ee1315191a6ca44fef0c74dfef2","schema_version":"1.0","event_id":"sha256:f4ecf01673cadcb0841b64296eac2fbde0dd7ee1315191a6ca44fef0c74dfef2"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/NNT3O7CGW4HENX4UUA62YX7FIS/bundle.json","state_url":"https://pith.science/pith/NNT3O7CGW4HENX4UUA62YX7FIS/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/NNT3O7CGW4HENX4UUA62YX7FIS/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":"2026-06-19T23:41:28Z","links":{"resolver":"https://pith.science/pith/NNT3O7CGW4HENX4UUA62YX7FIS","bundle":"https://pith.science/pith/NNT3O7CGW4HENX4UUA62YX7FIS/bundle.json","state":"https://pith.science/pith/NNT3O7CGW4HENX4UUA62YX7FIS/state.json","well_known_bundle":"https://pith.science/.well-known/pith/NNT3O7CGW4HENX4UUA62YX7FIS/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:NNT3O7CGW4HENX4UUA62YX7FIS","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":"4dbaa8164694c12c46754bf7ca83cf38826cef0140f346aadf3a514b39b8cd37","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2024-11-11T18:43:09Z","title_canon_sha256":"0969273f21c63bd92fada3ade50ccd44c7f1f46c6d935f4b6f9e96f1e2c27b64"},"schema_version":"1.0","source":{"id":"2411.07221","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2411.07221","created_at":"2026-06-19T16:10:29Z"},{"alias_kind":"arxiv_version","alias_value":"2411.07221v2","created_at":"2026-06-19T16:10:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.07221","created_at":"2026-06-19T16:10:29Z"},{"alias_kind":"pith_short_12","alias_value":"NNT3O7CGW4HE","created_at":"2026-06-19T16:10:29Z"},{"alias_kind":"pith_short_16","alias_value":"NNT3O7CGW4HENX4U","created_at":"2026-06-19T16:10:29Z"},{"alias_kind":"pith_short_8","alias_value":"NNT3O7CG","created_at":"2026-06-19T16:10:29Z"}],"graph_snapshots":[{"event_id":"sha256:f4ecf01673cadcb0841b64296eac2fbde0dd7ee1315191a6ca44fef0c74dfef2","target":"graph","created_at":"2026-06-19T16:10:29Z","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":"This results in templates for identification in the mediation setting, generally useful identification techniques, and perhaps most importantly a synthesis and substantial extension of shadow variable theory."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"Identification in both model classes rests on specific conditional independence assumptions (for self-separated) or the presence and properties of shadow variables (for self-connected) that are not automatically satisfied by the data and must be justified externally; the abstract states these are required for the identification results to hold."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"Introduces self-separated and self-connected missingness models for mediator and outcome missingness in mediation analysis, enabling identification via conditional independences or shadow variables and extending shadow variable theory."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"Identification of mediation effects remains possible when both mediator and outcome are missing under self-separated conditional independence or self-connected shadow variable models."}],"snapshot_sha256":"92acb8823fea37a5eb5a6deac9e6a402d036c8c3a32dd5c0b9c8ec784223682e"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2411.07221/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Missing data is a common challenge in studying treatment effects. In the context of mediation analysis, this paper addresses missingness in the mediator and outcome, focusing on identification. We first consider self-separated missingness models where identification is achieved by conditional independence assumptions. This model class is somewhat limited as it is constrained by the need to remove a certain number of connections from the model. We then turn to self-connected missingness models where identification relies on information from shadow variables. This model class turns out to contai","authors_text":"Elizabeth A. Stuart, Fan Yang, Grace V. Ringlein, Razieh Nabi, Trang Quynh Nguyen","cross_cats":[],"headline":"Identification of mediation effects remains possible when both mediator and outcome are missing under self-separated conditional independence or self-connected shadow variable models.","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2024-11-11T18:43:09Z","title":"Self-separated and self-connected models for mediator and outcome missingness in mediation analysis"},"references":{"count":38,"internal_anchors":0,"resolved_work":38,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"The Moderator - Mediator Variable Distinction in Social Psychological Research : Conceptual , Strategic , and Statistical Considerations","work_id":"f91d87c6-1792-4d8b-958e-53abd275e013","year":1986},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"Inference for natural mediation effects under case-cohort sampling with applications in identifying COVID -19 vaccine correlates of protection","work_id":"1e739fdb-857e-4ac9-8f66-2b7fa98f4c99","year":2021},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"Rohit Bhattacharya, Razieh Nabi, Ilya Shpitser, and James M. Robins. Identification In Missing Data Models Represented By Directed Acyclic Graphs . In Proceedings of The 35th Uncertainty in Artificial","work_id":"7df54878-70ab-4d60-b18a-92bbb2c4690f","year":2020},{"cited_arxiv_id":"","doi":"10.1097/ede.0b013e31818ef366","is_internal_anchor":false,"ref_index":4,"title":"Cole and Constantine E","work_id":"d5e5a75a-1343-4f69-a1ae-20f6b8c88a3f","year":2009},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":5,"title":"Ghazaleh Dashti, Katherine J","work_id":"6545b2f3-1cc8-4113-915f-73b91ba38e3b","year":2024}],"snapshot_sha256":"6ff32f7c4a53b284bf35add646d71e67f647048f92a61171a0ac0fad43cdb1aa"},"source":{"id":"2411.07221","kind":"arxiv","version":2},"verdict":{"created_at":"2026-05-23T17:22:59.362603Z","id":"cf89d3e6-63a5-4686-9a50-a7c344398a6f","model_set":{"reader":"grok-4.3"},"one_line_summary":"Introduces self-separated and self-connected missingness models for mediator and outcome missingness in mediation analysis, enabling identification via conditional independences or shadow variables and extending shadow variable theory.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"Identification of mediation effects remains possible when both mediator and outcome are missing under self-separated conditional independence or self-connected shadow variable models.","strongest_claim":"This results in templates for identification in the mediation setting, generally useful identification techniques, and perhaps most importantly a synthesis and substantial extension of shadow variable theory.","weakest_assumption":"Identification in both model classes rests on specific conditional independence assumptions (for self-separated) or the presence and properties of shadow variables (for self-connected) that are not automatically satisfied by the data and must be justified externally; the abstract states these are required for the identification results to hold."}},"verdict_id":"cf89d3e6-63a5-4686-9a50-a7c344398a6f"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:af7a90d85777fa4b5fbe148bf5afc873ff07460742d4c490fc0a78e0a5bb230c","target":"record","created_at":"2026-06-19T16:10:29Z","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":"4dbaa8164694c12c46754bf7ca83cf38826cef0140f346aadf3a514b39b8cd37","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ME","submitted_at":"2024-11-11T18:43:09Z","title_canon_sha256":"0969273f21c63bd92fada3ade50ccd44c7f1f46c6d935f4b6f9e96f1e2c27b64"},"schema_version":"1.0","source":{"id":"2411.07221","kind":"arxiv","version":2}},"canonical_sha256":"6b67b77c46b70e46df94a03dac5fe5449044ce0d618b4f1a002d626292eb9750","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6b67b77c46b70e46df94a03dac5fe5449044ce0d618b4f1a002d626292eb9750","first_computed_at":"2026-06-19T16:10:29.010764Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-19T16:10:29.010764Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"w345U0hspAdFEr6hOxG8Q6PaGozv4QarUJNWsJM5OJv4VHHm5ZbfO0DLRnFc6DLtA7T6Oo0T+n6VC3tZTGcnBA==","signature_status":"signed_v1","signed_at":"2026-06-19T16:10:29.011196Z","signed_message":"canonical_sha256_bytes"},"source_id":"2411.07221","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:af7a90d85777fa4b5fbe148bf5afc873ff07460742d4c490fc0a78e0a5bb230c","sha256:f4ecf01673cadcb0841b64296eac2fbde0dd7ee1315191a6ca44fef0c74dfef2"],"state_sha256":"d2fd4445fd84e03f3dd432e0a4efdd846d5de99837fdafe90f9888f12b388747"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Hqh9hfuYmQ+njIn8iGrV5Hg0iAkVpO40z984uIGaNVjmapbK0TeXS030trrTFZkfdr73qIQxsiN0J0O7DkYLAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-19T23:41:28.193936Z","bundle_sha256":"9fa98c1002d7b8aa988e0bafbe1a3d3c42a9c5792a852267bdf804f76b5e3f6e"}}