{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:R6QZASJZS2ZHNWOSET5B7SI2UL","short_pith_number":"pith:R6QZASJZ","canonical_record":{"source":{"id":"2605.13168","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2026-05-13T08:30:44Z","cross_cats_sorted":[],"title_canon_sha256":"8ca80557e9389a3ef95907e09a623541ec010085a551ee9551d5a7aaac532fc1","abstract_canon_sha256":"c9577284f891672de977acfdd19debabb8903b39172c246c218089e452c62fdb"},"schema_version":"1.0"},"canonical_sha256":"8fa190493996b276d9d224fa1fc91aa2c3edfd1a7a1312cbc37ffc7ecb95df24","source":{"kind":"arxiv","id":"2605.13168","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.13168","created_at":"2026-05-18T03:08:56Z"},{"alias_kind":"arxiv_version","alias_value":"2605.13168v1","created_at":"2026-05-18T03:08:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.13168","created_at":"2026-05-18T03:08:56Z"},{"alias_kind":"pith_short_12","alias_value":"R6QZASJZS2ZH","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"R6QZASJZS2ZHNWOS","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"R6QZASJZ","created_at":"2026-05-18T12:33:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:R6QZASJZS2ZHNWOSET5B7SI2UL","target":"record","payload":{"canonical_record":{"source":{"id":"2605.13168","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2026-05-13T08:30:44Z","cross_cats_sorted":[],"title_canon_sha256":"8ca80557e9389a3ef95907e09a623541ec010085a551ee9551d5a7aaac532fc1","abstract_canon_sha256":"c9577284f891672de977acfdd19debabb8903b39172c246c218089e452c62fdb"},"schema_version":"1.0"},"canonical_sha256":"8fa190493996b276d9d224fa1fc91aa2c3edfd1a7a1312cbc37ffc7ecb95df24","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:08:56.663745Z","signature_b64":"9bdq/EeYp6ThR/xyuctFtxNS7P9xV/hC3B1RM8mz8v4CvI/0yE3/A836wkEHyyDU+zHbxFcRngflot0oBGz7Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8fa190493996b276d9d224fa1fc91aa2c3edfd1a7a1312cbc37ffc7ecb95df24","last_reissued_at":"2026-05-18T03:08:56.662895Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:08:56.662895Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.13168","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T03:08:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"iN+8qkjW5ExYKvIH3AjWXeM2OM5E1lweYtw9zZTccXCxqhPYRw6RvttHi9Nx5/I0BEVyJCmfZZUrF5uNEYK7Ag==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T07:30:05.442256Z"},"content_sha256":"26e290052cec134c6458421f3f64cfb92f1d583e20e4b1f0c774bf6235a561e4","schema_version":"1.0","event_id":"sha256:26e290052cec134c6458421f3f64cfb92f1d583e20e4b1f0c774bf6235a561e4"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:R6QZASJZS2ZHNWOSET5B7SI2UL","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Variance-Aware Estimation and Inference for Michaelis--Menten Models with Heteroscedastic Errors and Clustered Measurements","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"A variance-aware procedure using simple working models stabilizes Michaelis-Menten estimates of Km and Vmax when errors vary with concentration or data are clustered.","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Ah Young Jeong, Mijeong Kim, Minkyoung Cha","submitted_at":"2026-05-13T08:30:44Z","abstract_excerpt":"Michaelis--Menten analysis is often conducted by nonlinear least squares under a constant-variance assumption, even though enzyme-kinetic data frequently display concentration-dependent heteroscedasticity and often include repeated or clustered measurements. We develop a variance-aware procedure for Michaelis--Menten estimation and inference that is motivated by conditional moment restrictions and implemented through simple conditionally Gaussian working models. For single curves, the method reduces to one-dimensional root finding for $K_m$ followed by closed-form plug-in updates for $V_{\\max}"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"simple variance-function and covariance modeling can stabilize original-scale Michaelis--Menten inference when variability changes with substrate concentration or measurements are clustered.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The prespecified working variance functions and conditionally Gaussian models sufficiently approximate the true error distribution and clustering structure without biasing the parameter estimates for Km and Vmax.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"A new estimation method for Michaelis-Menten models handles varying variance and clustered data through root-finding and plug-in updates, improving inference in simulations and real enzyme data.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"A variance-aware procedure using simple working models stabilizes Michaelis-Menten estimates of Km and Vmax when errors vary with concentration or data are clustered.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"9d2ccecbb9cdaea09fcec85b0a96c3136459a283579bfcdf95f6eebab789387b"},"source":{"id":"2605.13168","kind":"arxiv","version":1},"verdict":{"id":"d323aafb-35de-499e-b40c-83117088b4fd","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-14T18:26:02.333703Z","strongest_claim":"simple variance-function and covariance modeling can stabilize original-scale Michaelis--Menten inference when variability changes with substrate concentration or measurements are clustered.","one_line_summary":"A new estimation method for Michaelis-Menten models handles varying variance and clustered data through root-finding and plug-in updates, improving inference in simulations and real enzyme data.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The prespecified working variance functions and conditionally Gaussian models sufficiently approximate the true error distribution and clustering structure without biasing the parameter estimates for Km and Vmax.","pith_extraction_headline":"A variance-aware procedure using simple working models stabilizes Michaelis-Menten estimates of Km and Vmax when errors vary with concentration or data are clustered."},"references":{"count":25,"sample":[{"doi":"","year":1913,"title":"L. Michaelis, M. L. Menten, Die kinetik der invertinwirkung, Biochem. Z. 49 (1913) 333–369","work_id":"6eac3cbc-7443-40ed-9ced-3ce6238c5493","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"10.1021/bi201284u","year":1913,"title":"K.A.Johnson, R.S.Goody, TheoriginalMichaelisconstant: translation of the 1913 Michaelis–Menten paper, Biochemistry 50 (39) (2011) 8264– 8269.doi:10.1021/bi201284u","work_id":"536917fc-40bf-4b01-99a2-5b2744eac2be","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"10.1042/bj0800324","year":1961,"title":"G. N. Wilkinson, Statistical estimations in enzyme kinetics, Biochem. J. 80 (2) (1961) 324–332.doi:10.1042/BJ0800324","work_id":"6f84e017-08ce-460f-a0fa-2c79dd339c07","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"10.1042/bj1390715","year":1974,"title":"R. Eisenthal, A. Cornish-Bowden, The direct linear plot: A new graph- ical procedure for estimating enzyme kinetic parameters, Biochem. J. 139 (3) (1974) 715–720.doi:10.1042/BJ1390715","work_id":"feb82108-42bd-48b5-9338-0654f5951d95","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"10.1042/bj1390721","year":1974,"title":"A. Cornish-Bowden, R. Eisenthal, Statistical considerations in the esti- mation of enzyme kinetic parameters by the direct linear plot and other methods, Biochem.J.139(3)(1974)721–730.doi:10.1042/BJ13","work_id":"3548cf15-9718-4ef1-a92b-ec349aad88d0","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":25,"snapshot_sha256":"0b94555871859cce7c59b300008ad275a09831b1c5502027cd5fa1022269204e","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":"d323aafb-35de-499e-b40c-83117088b4fd"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T03:08:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hWmpoc1litH/yxNumi3nq9jAvkv9PoPrmv635/7YJxDr/MwwqkS1r8KqkAFuo/3RBXfa2Cs+jeA95M0j98nsAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T07:30:05.443121Z"},"content_sha256":"66c1a186db206a453a20968c7a184df097098a63dc39287305e1487031aebef3","schema_version":"1.0","event_id":"sha256:66c1a186db206a453a20968c7a184df097098a63dc39287305e1487031aebef3"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/R6QZASJZS2ZHNWOSET5B7SI2UL/bundle.json","state_url":"https://pith.science/pith/R6QZASJZS2ZHNWOSET5B7SI2UL/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/R6QZASJZS2ZHNWOSET5B7SI2UL/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":"2026-05-27T07:30:05Z","links":{"resolver":"https://pith.science/pith/R6QZASJZS2ZHNWOSET5B7SI2UL","bundle":"https://pith.science/pith/R6QZASJZS2ZHNWOSET5B7SI2UL/bundle.json","state":"https://pith.science/pith/R6QZASJZS2ZHNWOSET5B7SI2UL/state.json","well_known_bundle":"https://pith.science/.well-known/pith/R6QZASJZS2ZHNWOSET5B7SI2UL/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:R6QZASJZS2ZHNWOSET5B7SI2UL","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":"c9577284f891672de977acfdd19debabb8903b39172c246c218089e452c62fdb","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2026-05-13T08:30:44Z","title_canon_sha256":"8ca80557e9389a3ef95907e09a623541ec010085a551ee9551d5a7aaac532fc1"},"schema_version":"1.0","source":{"id":"2605.13168","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.13168","created_at":"2026-05-18T03:08:56Z"},{"alias_kind":"arxiv_version","alias_value":"2605.13168v1","created_at":"2026-05-18T03:08:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.13168","created_at":"2026-05-18T03:08:56Z"},{"alias_kind":"pith_short_12","alias_value":"R6QZASJZS2ZH","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"R6QZASJZS2ZHNWOS","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"R6QZASJZ","created_at":"2026-05-18T12:33:37Z"}],"graph_snapshots":[{"event_id":"sha256:66c1a186db206a453a20968c7a184df097098a63dc39287305e1487031aebef3","target":"graph","created_at":"2026-05-18T03:08:56Z","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":"simple variance-function and covariance modeling can stabilize original-scale Michaelis--Menten inference when variability changes with substrate concentration or measurements are clustered."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"The prespecified working variance functions and conditionally Gaussian models sufficiently approximate the true error distribution and clustering structure without biasing the parameter estimates for Km and Vmax."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"A new estimation method for Michaelis-Menten models handles varying variance and clustered data through root-finding and plug-in updates, improving inference in simulations and real enzyme data."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"A variance-aware procedure using simple working models stabilizes Michaelis-Menten estimates of Km and Vmax when errors vary with concentration or data are clustered."}],"snapshot_sha256":"9d2ccecbb9cdaea09fcec85b0a96c3136459a283579bfcdf95f6eebab789387b"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Michaelis--Menten analysis is often conducted by nonlinear least squares under a constant-variance assumption, even though enzyme-kinetic data frequently display concentration-dependent heteroscedasticity and often include repeated or clustered measurements. We develop a variance-aware procedure for Michaelis--Menten estimation and inference that is motivated by conditional moment restrictions and implemented through simple conditionally Gaussian working models. For single curves, the method reduces to one-dimensional root finding for $K_m$ followed by closed-form plug-in updates for $V_{\\max}","authors_text":"Ah Young Jeong, Mijeong Kim, Minkyoung Cha","cross_cats":[],"headline":"A variance-aware procedure using simple working models stabilizes Michaelis-Menten estimates of Km and Vmax when errors vary with concentration or data are clustered.","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2026-05-13T08:30:44Z","title":"Variance-Aware Estimation and Inference for Michaelis--Menten Models with Heteroscedastic Errors and Clustered Measurements"},"references":{"count":25,"internal_anchors":0,"resolved_work":25,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"L. Michaelis, M. L. Menten, Die kinetik der invertinwirkung, Biochem. Z. 49 (1913) 333–369","work_id":"6eac3cbc-7443-40ed-9ced-3ce6238c5493","year":1913},{"cited_arxiv_id":"","doi":"10.1021/bi201284u","is_internal_anchor":false,"ref_index":2,"title":"K.A.Johnson, R.S.Goody, TheoriginalMichaelisconstant: translation of the 1913 Michaelis–Menten paper, Biochemistry 50 (39) (2011) 8264– 8269.doi:10.1021/bi201284u","work_id":"536917fc-40bf-4b01-99a2-5b2744eac2be","year":1913},{"cited_arxiv_id":"","doi":"10.1042/bj0800324","is_internal_anchor":false,"ref_index":3,"title":"G. N. Wilkinson, Statistical estimations in enzyme kinetics, Biochem. J. 80 (2) (1961) 324–332.doi:10.1042/BJ0800324","work_id":"6f84e017-08ce-460f-a0fa-2c79dd339c07","year":1961},{"cited_arxiv_id":"","doi":"10.1042/bj1390715","is_internal_anchor":false,"ref_index":4,"title":"R. Eisenthal, A. Cornish-Bowden, The direct linear plot: A new graph- ical procedure for estimating enzyme kinetic parameters, Biochem. J. 139 (3) (1974) 715–720.doi:10.1042/BJ1390715","work_id":"feb82108-42bd-48b5-9338-0654f5951d95","year":1974},{"cited_arxiv_id":"","doi":"10.1042/bj1390721","is_internal_anchor":false,"ref_index":5,"title":"A. Cornish-Bowden, R. Eisenthal, Statistical considerations in the esti- mation of enzyme kinetic parameters by the direct linear plot and other methods, Biochem.J.139(3)(1974)721–730.doi:10.1042/BJ13","work_id":"3548cf15-9718-4ef1-a92b-ec349aad88d0","year":1974}],"snapshot_sha256":"0b94555871859cce7c59b300008ad275a09831b1c5502027cd5fa1022269204e"},"source":{"id":"2605.13168","kind":"arxiv","version":1},"verdict":{"created_at":"2026-05-14T18:26:02.333703Z","id":"d323aafb-35de-499e-b40c-83117088b4fd","model_set":{"reader":"grok-4.3"},"one_line_summary":"A new estimation method for Michaelis-Menten models handles varying variance and clustered data through root-finding and plug-in updates, improving inference in simulations and real enzyme data.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"A variance-aware procedure using simple working models stabilizes Michaelis-Menten estimates of Km and Vmax when errors vary with concentration or data are clustered.","strongest_claim":"simple variance-function and covariance modeling can stabilize original-scale Michaelis--Menten inference when variability changes with substrate concentration or measurements are clustered.","weakest_assumption":"The prespecified working variance functions and conditionally Gaussian models sufficiently approximate the true error distribution and clustering structure without biasing the parameter estimates for Km and Vmax."}},"verdict_id":"d323aafb-35de-499e-b40c-83117088b4fd"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:26e290052cec134c6458421f3f64cfb92f1d583e20e4b1f0c774bf6235a561e4","target":"record","created_at":"2026-05-18T03:08:56Z","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":"c9577284f891672de977acfdd19debabb8903b39172c246c218089e452c62fdb","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2026-05-13T08:30:44Z","title_canon_sha256":"8ca80557e9389a3ef95907e09a623541ec010085a551ee9551d5a7aaac532fc1"},"schema_version":"1.0","source":{"id":"2605.13168","kind":"arxiv","version":1}},"canonical_sha256":"8fa190493996b276d9d224fa1fc91aa2c3edfd1a7a1312cbc37ffc7ecb95df24","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8fa190493996b276d9d224fa1fc91aa2c3edfd1a7a1312cbc37ffc7ecb95df24","first_computed_at":"2026-05-18T03:08:56.662895Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T03:08:56.662895Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"9bdq/EeYp6ThR/xyuctFtxNS7P9xV/hC3B1RM8mz8v4CvI/0yE3/A836wkEHyyDU+zHbxFcRngflot0oBGz7Aw==","signature_status":"signed_v1","signed_at":"2026-05-18T03:08:56.663745Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.13168","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:26e290052cec134c6458421f3f64cfb92f1d583e20e4b1f0c774bf6235a561e4","sha256:66c1a186db206a453a20968c7a184df097098a63dc39287305e1487031aebef3"],"state_sha256":"f6d516b76ce7491473c8f2e9f7842e879315ed9e75eacdb7039cd3e2054994ff"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xSsOveVC4ETKbcB2SSUl4+ljlQSIYjm7sBkkko7z0S2BAAcyaBqg8i1MSMeNLpoR4Li7X0DSu4ZuKEUg4TgxAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-27T07:30:05.447884Z","bundle_sha256":"c5425196dc30df76c425bfb4ed391cdea4b4b4755044f2b0141d6b6b36a50901"}}