{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2015:WDBEDOHO72BOOFEGW5X4K3WWPE","short_pith_number":"pith:WDBEDOHO","canonical_record":{"source":{"id":"1502.07766","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2015-02-03T01:02:24Z","cross_cats_sorted":[],"title_canon_sha256":"987a1910655bceda4934b6c81bbed282d205e2e3f461de479c8e37515451fd85","abstract_canon_sha256":"0eeec08d998d97c7a398b52af936836a443fa0cfc336ed8bd71cd85c8e93cdb1"},"schema_version":"1.0"},"canonical_sha256":"b0c241b8eefe82e71486b76fc56ed6790e296e7d13225f72504e33d957bbd114","source":{"kind":"arxiv","id":"1502.07766","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1502.07766","created_at":"2026-05-18T01:20:46Z"},{"alias_kind":"arxiv_version","alias_value":"1502.07766v1","created_at":"2026-05-18T01:20:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1502.07766","created_at":"2026-05-18T01:20:46Z"},{"alias_kind":"pith_short_12","alias_value":"WDBEDOHO72BO","created_at":"2026-05-18T12:29:47Z"},{"alias_kind":"pith_short_16","alias_value":"WDBEDOHO72BOOFEG","created_at":"2026-05-18T12:29:47Z"},{"alias_kind":"pith_short_8","alias_value":"WDBEDOHO","created_at":"2026-05-18T12:29:47Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2015:WDBEDOHO72BOOFEGW5X4K3WWPE","target":"record","payload":{"canonical_record":{"source":{"id":"1502.07766","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2015-02-03T01:02:24Z","cross_cats_sorted":[],"title_canon_sha256":"987a1910655bceda4934b6c81bbed282d205e2e3f461de479c8e37515451fd85","abstract_canon_sha256":"0eeec08d998d97c7a398b52af936836a443fa0cfc336ed8bd71cd85c8e93cdb1"},"schema_version":"1.0"},"canonical_sha256":"b0c241b8eefe82e71486b76fc56ed6790e296e7d13225f72504e33d957bbd114","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:20:46.925564Z","signature_b64":"4QkyjCqKREXgekcaNwP1niBodwYtcg5eNbC4/c2/KpKp0tHuI++RCtr1NkGSX/oJ6r1RtuyC4dpZcDnXyOOZCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b0c241b8eefe82e71486b76fc56ed6790e296e7d13225f72504e33d957bbd114","last_reissued_at":"2026-05-18T01:20:46.925041Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:20:46.925041Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1502.07766","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":"2026-05-18T01:20:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ehIMunRlFyBL++PwQ6qFWxnB+/INJ9mfBJatqag9n/XtSCFP1Rq8s1LkDbpJ4tL9jpwq855AquF6E/ddMwtKDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T09:45:25.341906Z"},"content_sha256":"e2689292eb9e16f43e12a98b8cd10141da17a905ba2ef8745690f9b91c3fa198","schema_version":"1.0","event_id":"sha256:e2689292eb9e16f43e12a98b8cd10141da17a905ba2ef8745690f9b91c3fa198"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2015:WDBEDOHO72BOOFEGW5X4K3WWPE","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Semiparametric forecasting and filtering: correcting low-dimensional model error in parametric models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"John Harlim, Tyrus Berry","submitted_at":"2015-02-03T01:02:24Z","abstract_excerpt":"Semiparametric forecasting and filtering are introduced as a method of addressing model errors arising from unresolved physical phenomena. While traditional parametric models are able to learn high-dimensional systems from small data sets, their rigid parametric structure makes them vulnerable to model error. On the other hand, nonparametric models have a very flexible structure, but they suffer from the curse-of-dimensionality and are not practical for high-dimensional systems. The semiparametric approach loosens the structure of a parametric model by fitting a data-driven nonparametric model"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1502.07766","kind":"arxiv","version":1},"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":"2026-05-18T01:20:46Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"AaFDYeWCsayJZE6rnBDArT2B0efCQUdj0hsgcijrFewrva09zOjuRSvwWrT5IkgqN+2XDdFerHA1M0PL0p4mCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T09:45:25.342254Z"},"content_sha256":"630f475f4334f0e56e46afc126e03fb5f7891c583966ea1fb29b01609d52c995","schema_version":"1.0","event_id":"sha256:630f475f4334f0e56e46afc126e03fb5f7891c583966ea1fb29b01609d52c995"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/WDBEDOHO72BOOFEGW5X4K3WWPE/bundle.json","state_url":"https://pith.science/pith/WDBEDOHO72BOOFEGW5X4K3WWPE/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/WDBEDOHO72BOOFEGW5X4K3WWPE/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":"2026-06-02T09:45:25Z","links":{"resolver":"https://pith.science/pith/WDBEDOHO72BOOFEGW5X4K3WWPE","bundle":"https://pith.science/pith/WDBEDOHO72BOOFEGW5X4K3WWPE/bundle.json","state":"https://pith.science/pith/WDBEDOHO72BOOFEGW5X4K3WWPE/state.json","well_known_bundle":"https://pith.science/.well-known/pith/WDBEDOHO72BOOFEGW5X4K3WWPE/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:WDBEDOHO72BOOFEGW5X4K3WWPE","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":"0eeec08d998d97c7a398b52af936836a443fa0cfc336ed8bd71cd85c8e93cdb1","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2015-02-03T01:02:24Z","title_canon_sha256":"987a1910655bceda4934b6c81bbed282d205e2e3f461de479c8e37515451fd85"},"schema_version":"1.0","source":{"id":"1502.07766","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1502.07766","created_at":"2026-05-18T01:20:46Z"},{"alias_kind":"arxiv_version","alias_value":"1502.07766v1","created_at":"2026-05-18T01:20:46Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1502.07766","created_at":"2026-05-18T01:20:46Z"},{"alias_kind":"pith_short_12","alias_value":"WDBEDOHO72BO","created_at":"2026-05-18T12:29:47Z"},{"alias_kind":"pith_short_16","alias_value":"WDBEDOHO72BOOFEG","created_at":"2026-05-18T12:29:47Z"},{"alias_kind":"pith_short_8","alias_value":"WDBEDOHO","created_at":"2026-05-18T12:29:47Z"}],"graph_snapshots":[{"event_id":"sha256:630f475f4334f0e56e46afc126e03fb5f7891c583966ea1fb29b01609d52c995","target":"graph","created_at":"2026-05-18T01:20:46Z","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":"Semiparametric forecasting and filtering are introduced as a method of addressing model errors arising from unresolved physical phenomena. While traditional parametric models are able to learn high-dimensional systems from small data sets, their rigid parametric structure makes them vulnerable to model error. On the other hand, nonparametric models have a very flexible structure, but they suffer from the curse-of-dimensionality and are not practical for high-dimensional systems. The semiparametric approach loosens the structure of a parametric model by fitting a data-driven nonparametric model","authors_text":"John Harlim, Tyrus Berry","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2015-02-03T01:02:24Z","title":"Semiparametric forecasting and filtering: correcting low-dimensional model error in parametric models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1502.07766","kind":"arxiv","version":1},"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:e2689292eb9e16f43e12a98b8cd10141da17a905ba2ef8745690f9b91c3fa198","target":"record","created_at":"2026-05-18T01:20:46Z","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":"0eeec08d998d97c7a398b52af936836a443fa0cfc336ed8bd71cd85c8e93cdb1","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2015-02-03T01:02:24Z","title_canon_sha256":"987a1910655bceda4934b6c81bbed282d205e2e3f461de479c8e37515451fd85"},"schema_version":"1.0","source":{"id":"1502.07766","kind":"arxiv","version":1}},"canonical_sha256":"b0c241b8eefe82e71486b76fc56ed6790e296e7d13225f72504e33d957bbd114","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b0c241b8eefe82e71486b76fc56ed6790e296e7d13225f72504e33d957bbd114","first_computed_at":"2026-05-18T01:20:46.925041Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:20:46.925041Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"4QkyjCqKREXgekcaNwP1niBodwYtcg5eNbC4/c2/KpKp0tHuI++RCtr1NkGSX/oJ6r1RtuyC4dpZcDnXyOOZCg==","signature_status":"signed_v1","signed_at":"2026-05-18T01:20:46.925564Z","signed_message":"canonical_sha256_bytes"},"source_id":"1502.07766","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e2689292eb9e16f43e12a98b8cd10141da17a905ba2ef8745690f9b91c3fa198","sha256:630f475f4334f0e56e46afc126e03fb5f7891c583966ea1fb29b01609d52c995"],"state_sha256":"d54d3f72bbe797c8e7599ab1e8c804d0bfa9036f04b4caf5152ff3240b9f9e96"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kHtK+le5QJ7b3pdRDktrGv/XH0soOU2he6TAnwolMAAdAjh4mdZReCq+7Gv9fbvKA403kFP91Gg/A651XgXNCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-02T09:45:25.344545Z","bundle_sha256":"44db44d672a0a4d26e47507ed3042cf53aea59543bee957db332952998984fde"}}