{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:CV3PKK4YY2OGRU2YOIZDQOXDYS","short_pith_number":"pith:CV3PKK4Y","canonical_record":{"source":{"id":"1711.00636","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2017-11-02T07:27:19Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"dff49bee68088eec7df7fa662dc1f18abd7d2547f2816f2e4a50761b62300a5b","abstract_canon_sha256":"dbd9db5099086ebd2aa02e2ec005e63ccbf82bd78451eded294e68a2ee332910"},"schema_version":"1.0"},"canonical_sha256":"1576f52b98c69c68d3587232383ae3c4b1b383ffacc5c8724ac2aba323fe81a8","source":{"kind":"arxiv","id":"1711.00636","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1711.00636","created_at":"2026-05-18T00:24:09Z"},{"alias_kind":"arxiv_version","alias_value":"1711.00636v2","created_at":"2026-05-18T00:24:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.00636","created_at":"2026-05-18T00:24:09Z"},{"alias_kind":"pith_short_12","alias_value":"CV3PKK4YY2OG","created_at":"2026-05-18T12:31:10Z"},{"alias_kind":"pith_short_16","alias_value":"CV3PKK4YY2OGRU2Y","created_at":"2026-05-18T12:31:10Z"},{"alias_kind":"pith_short_8","alias_value":"CV3PKK4Y","created_at":"2026-05-18T12:31:10Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:CV3PKK4YY2OGRU2YOIZDQOXDYS","target":"record","payload":{"canonical_record":{"source":{"id":"1711.00636","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2017-11-02T07:27:19Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"dff49bee68088eec7df7fa662dc1f18abd7d2547f2816f2e4a50761b62300a5b","abstract_canon_sha256":"dbd9db5099086ebd2aa02e2ec005e63ccbf82bd78451eded294e68a2ee332910"},"schema_version":"1.0"},"canonical_sha256":"1576f52b98c69c68d3587232383ae3c4b1b383ffacc5c8724ac2aba323fe81a8","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:24:09.670254Z","signature_b64":"JI33ZrfkYX9jld4Gt+9ldnGfAY01U6mRWH+Ar20OGMJg+0la4a+lpWKi4RCOTi5kkUFjspUI0CZ3DMYRiMe3Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1576f52b98c69c68d3587232383ae3c4b1b383ffacc5c8724ac2aba323fe81a8","last_reissued_at":"2026-05-18T00:24:09.669507Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:24:09.669507Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1711.00636","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":"2026-05-18T00:24:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"L9Tl1+BkOBgmYLQ7c4vptjRHEOicjT2Bt0KL9g0JuuSGih9Hmt5463lurcysfC68Bjmzx0/Muf3uROyNk5hiCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-24T03:18:16.743293Z"},"content_sha256":"9e6b21c39a78a17d96490aacb4ab72a30a97b2a61631459d12df1d7b953014b7","schema_version":"1.0","event_id":"sha256:9e6b21c39a78a17d96490aacb4ab72a30a97b2a61631459d12df1d7b953014b7"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:CV3PKK4YY2OGRU2YOIZDQOXDYS","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Bayesian Recurrent Neural Network Models for Forecasting and Quantifying Uncertainty in Spatial-Temporal Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"stat.ME","authors_text":"Christopher K. Wikle, Patrick L. McDermott","submitted_at":"2017-11-02T07:27:19Z","abstract_excerpt":"Recurrent neural networks (RNNs) are nonlinear dynamical models commonly used in the machine learning and dynamical systems literature to represent complex dynamical or sequential relationships between variables. More recently, as deep learning models have become more common, RNNs have been used to forecast increasingly complicated systems. Dynamical spatio-temporal processes represent a class of complex systems that can potentially benefit from these types of models. Although the RNN literature is expansive and highly developed, uncertainty quantification is often ignored. Even when considere"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.00636","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","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":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:24:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ly4XfzvOM2NuMdJOtnh3yXjArjbqMp4S36QsrX5xiE9XCJ9Or2b2JzLL5ieoHQQbm81wlLrPcHYbTeyIf0mfBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-24T03:18:16.743652Z"},"content_sha256":"66eb1bd31b4e846e6e8d3469b8d7a9830e2d425c1e7e0b7536bd6ab174b4770b","schema_version":"1.0","event_id":"sha256:66eb1bd31b4e846e6e8d3469b8d7a9830e2d425c1e7e0b7536bd6ab174b4770b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/CV3PKK4YY2OGRU2YOIZDQOXDYS/bundle.json","state_url":"https://pith.science/pith/CV3PKK4YY2OGRU2YOIZDQOXDYS/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/CV3PKK4YY2OGRU2YOIZDQOXDYS/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":"2026-06-24T03:18:16Z","links":{"resolver":"https://pith.science/pith/CV3PKK4YY2OGRU2YOIZDQOXDYS","bundle":"https://pith.science/pith/CV3PKK4YY2OGRU2YOIZDQOXDYS/bundle.json","state":"https://pith.science/pith/CV3PKK4YY2OGRU2YOIZDQOXDYS/state.json","well_known_bundle":"https://pith.science/.well-known/pith/CV3PKK4YY2OGRU2YOIZDQOXDYS/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:CV3PKK4YY2OGRU2YOIZDQOXDYS","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":"dbd9db5099086ebd2aa02e2ec005e63ccbf82bd78451eded294e68a2ee332910","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2017-11-02T07:27:19Z","title_canon_sha256":"dff49bee68088eec7df7fa662dc1f18abd7d2547f2816f2e4a50761b62300a5b"},"schema_version":"1.0","source":{"id":"1711.00636","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1711.00636","created_at":"2026-05-18T00:24:09Z"},{"alias_kind":"arxiv_version","alias_value":"1711.00636v2","created_at":"2026-05-18T00:24:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.00636","created_at":"2026-05-18T00:24:09Z"},{"alias_kind":"pith_short_12","alias_value":"CV3PKK4YY2OG","created_at":"2026-05-18T12:31:10Z"},{"alias_kind":"pith_short_16","alias_value":"CV3PKK4YY2OGRU2Y","created_at":"2026-05-18T12:31:10Z"},{"alias_kind":"pith_short_8","alias_value":"CV3PKK4Y","created_at":"2026-05-18T12:31:10Z"}],"graph_snapshots":[{"event_id":"sha256:66eb1bd31b4e846e6e8d3469b8d7a9830e2d425c1e7e0b7536bd6ab174b4770b","target":"graph","created_at":"2026-05-18T00:24:09Z","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":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Recurrent neural networks (RNNs) are nonlinear dynamical models commonly used in the machine learning and dynamical systems literature to represent complex dynamical or sequential relationships between variables. More recently, as deep learning models have become more common, RNNs have been used to forecast increasingly complicated systems. Dynamical spatio-temporal processes represent a class of complex systems that can potentially benefit from these types of models. Although the RNN literature is expansive and highly developed, uncertainty quantification is often ignored. Even when considere","authors_text":"Christopher K. Wikle, Patrick L. McDermott","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2017-11-02T07:27:19Z","title":"Bayesian Recurrent Neural Network Models for Forecasting and Quantifying Uncertainty in Spatial-Temporal Data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.00636","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:9e6b21c39a78a17d96490aacb4ab72a30a97b2a61631459d12df1d7b953014b7","target":"record","created_at":"2026-05-18T00:24:09Z","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":"dbd9db5099086ebd2aa02e2ec005e63ccbf82bd78451eded294e68a2ee332910","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2017-11-02T07:27:19Z","title_canon_sha256":"dff49bee68088eec7df7fa662dc1f18abd7d2547f2816f2e4a50761b62300a5b"},"schema_version":"1.0","source":{"id":"1711.00636","kind":"arxiv","version":2}},"canonical_sha256":"1576f52b98c69c68d3587232383ae3c4b1b383ffacc5c8724ac2aba323fe81a8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1576f52b98c69c68d3587232383ae3c4b1b383ffacc5c8724ac2aba323fe81a8","first_computed_at":"2026-05-18T00:24:09.669507Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:24:09.669507Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"JI33ZrfkYX9jld4Gt+9ldnGfAY01U6mRWH+Ar20OGMJg+0la4a+lpWKi4RCOTi5kkUFjspUI0CZ3DMYRiMe3Aw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:24:09.670254Z","signed_message":"canonical_sha256_bytes"},"source_id":"1711.00636","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9e6b21c39a78a17d96490aacb4ab72a30a97b2a61631459d12df1d7b953014b7","sha256:66eb1bd31b4e846e6e8d3469b8d7a9830e2d425c1e7e0b7536bd6ab174b4770b"],"state_sha256":"637642bac9401d6c5b007386d22d2a99891878e0c0fb8df262eecd6a3b2ce718"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"n87MJxlkEjOhafTfSH+OVuQUdW4KkEGCkMZbmv0KeLSawrQwQyLaevp/Yy5ExU+KuYu3gO69ocjUaBtfUtC8Dw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-24T03:18:16.745637Z","bundle_sha256":"4606358b805d14acc3131540914aa1a41a0395be802743a8f4bdb45c0506857b"}}