{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:A5MIXTAPXSRIUFGQMWXSI3EBFD","short_pith_number":"pith:A5MIXTAP","canonical_record":{"source":{"id":"2005.09056","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2020-05-18T20:09:20Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"60fc370e386548e8d55c04885cc330ceee3532e206e552dbb7bd5cfcf24dc941","abstract_canon_sha256":"d55b63e9bdafde7f6e5e0c1ede7ba33fc700b30ad78f1945e8f9de9e5b995fe4"},"schema_version":"1.0"},"canonical_sha256":"07588bcc0fbca28a14d065af246c8128c90507010294d52e0a3981cf56cb20bd","source":{"kind":"arxiv","id":"2005.09056","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2005.09056","created_at":"2026-07-05T01:57:05Z"},{"alias_kind":"arxiv_version","alias_value":"2005.09056v1","created_at":"2026-07-05T01:57:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2005.09056","created_at":"2026-07-05T01:57:05Z"},{"alias_kind":"pith_short_12","alias_value":"A5MIXTAPXSRI","created_at":"2026-07-05T01:57:05Z"},{"alias_kind":"pith_short_16","alias_value":"A5MIXTAPXSRIUFGQ","created_at":"2026-07-05T01:57:05Z"},{"alias_kind":"pith_short_8","alias_value":"A5MIXTAP","created_at":"2026-07-05T01:57:05Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:A5MIXTAPXSRIUFGQMWXSI3EBFD","target":"record","payload":{"canonical_record":{"source":{"id":"2005.09056","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2020-05-18T20:09:20Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"60fc370e386548e8d55c04885cc330ceee3532e206e552dbb7bd5cfcf24dc941","abstract_canon_sha256":"d55b63e9bdafde7f6e5e0c1ede7ba33fc700b30ad78f1945e8f9de9e5b995fe4"},"schema_version":"1.0"},"canonical_sha256":"07588bcc0fbca28a14d065af246c8128c90507010294d52e0a3981cf56cb20bd","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:57:05.852671Z","signature_b64":"EV9Uq8kEB2hGgnHzFkbTPOkBRNeRXSvNaYVn9EbULWub9NZh+uLkezWczwUZWwU5K9gC6sqdcxHQIwdf/c8SDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"07588bcc0fbca28a14d065af246c8128c90507010294d52e0a3981cf56cb20bd","last_reissued_at":"2026-07-05T01:57:05.852187Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:57:05.852187Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2005.09056","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":"2026-07-05T01:57:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tJNH1sYVAYKzX2DLW40oHQgoD003OQOLv3n4iSek8kSESM2B2ng84qWl1Fa58ANHBCIv/xLfwtC6xyCd++ThBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T17:47:11.811882Z"},"content_sha256":"38d3aebd80afe2324af58dc4b26e087078d1f654abe70d18496fa1f096c35135","schema_version":"1.0","event_id":"sha256:38d3aebd80afe2324af58dc4b26e087078d1f654abe70d18496fa1f096c35135"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:A5MIXTAPXSRIUFGQMWXSI3EBFD","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Tropical and Extratropical Cyclone Detection Using Deep Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"eess.IV","authors_text":"Christina Kumler-Bonfanti, David Hall, Jebb Stewart, Mark Govett","submitted_at":"2020-05-18T20:09:20Z","abstract_excerpt":"Extracting valuable information from large sets of diverse meteorological data is a time-intensive process. Machine learning methods can help improve both speed and accuracy of this process. Specifically, deep learning image segmentation models using the U-Net structure perform faster and can identify areas missed by more restrictive approaches, such as expert hand-labeling and a priori heuristic methods. This paper discusses four different state-of-the-art U-Net models designed for detection of tropical and extratropical cyclone Regions Of Interest (ROI) from two separate input sources: total"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2005.09056","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2005.09056/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2026-07-05T01:57:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tNw2Zx7rE6sCkfbtW8Se2ynCP7raHUJ2Ra3TUViWhQUqE3Em0HI1XNizoEaR0Tr9ESvGm6/AV/Q0yOvH0EB5CQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T17:47:11.812413Z"},"content_sha256":"cb0b48ff241fdfd256ae7ac36d5e479dc52fafaedcd047474743a42f7410305d","schema_version":"1.0","event_id":"sha256:cb0b48ff241fdfd256ae7ac36d5e479dc52fafaedcd047474743a42f7410305d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/A5MIXTAPXSRIUFGQMWXSI3EBFD/bundle.json","state_url":"https://pith.science/pith/A5MIXTAPXSRIUFGQMWXSI3EBFD/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/A5MIXTAPXSRIUFGQMWXSI3EBFD/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":"2026-08-04T17:47:11Z","links":{"resolver":"https://pith.science/pith/A5MIXTAPXSRIUFGQMWXSI3EBFD","bundle":"https://pith.science/pith/A5MIXTAPXSRIUFGQMWXSI3EBFD/bundle.json","state":"https://pith.science/pith/A5MIXTAPXSRIUFGQMWXSI3EBFD/state.json","well_known_bundle":"https://pith.science/.well-known/pith/A5MIXTAPXSRIUFGQMWXSI3EBFD/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:A5MIXTAPXSRIUFGQMWXSI3EBFD","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":"d55b63e9bdafde7f6e5e0c1ede7ba33fc700b30ad78f1945e8f9de9e5b995fe4","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2020-05-18T20:09:20Z","title_canon_sha256":"60fc370e386548e8d55c04885cc330ceee3532e206e552dbb7bd5cfcf24dc941"},"schema_version":"1.0","source":{"id":"2005.09056","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2005.09056","created_at":"2026-07-05T01:57:05Z"},{"alias_kind":"arxiv_version","alias_value":"2005.09056v1","created_at":"2026-07-05T01:57:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2005.09056","created_at":"2026-07-05T01:57:05Z"},{"alias_kind":"pith_short_12","alias_value":"A5MIXTAPXSRI","created_at":"2026-07-05T01:57:05Z"},{"alias_kind":"pith_short_16","alias_value":"A5MIXTAPXSRIUFGQ","created_at":"2026-07-05T01:57:05Z"},{"alias_kind":"pith_short_8","alias_value":"A5MIXTAP","created_at":"2026-07-05T01:57:05Z"}],"graph_snapshots":[{"event_id":"sha256:cb0b48ff241fdfd256ae7ac36d5e479dc52fafaedcd047474743a42f7410305d","target":"graph","created_at":"2026-07-05T01:57:05Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2005.09056/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Extracting valuable information from large sets of diverse meteorological data is a time-intensive process. Machine learning methods can help improve both speed and accuracy of this process. Specifically, deep learning image segmentation models using the U-Net structure perform faster and can identify areas missed by more restrictive approaches, such as expert hand-labeling and a priori heuristic methods. This paper discusses four different state-of-the-art U-Net models designed for detection of tropical and extratropical cyclone Regions Of Interest (ROI) from two separate input sources: total","authors_text":"Christina Kumler-Bonfanti, David Hall, Jebb Stewart, Mark Govett","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2020-05-18T20:09:20Z","title":"Tropical and Extratropical Cyclone Detection Using Deep Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2005.09056","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":"sha256:38d3aebd80afe2324af58dc4b26e087078d1f654abe70d18496fa1f096c35135","target":"record","created_at":"2026-07-05T01:57:05Z","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":"d55b63e9bdafde7f6e5e0c1ede7ba33fc700b30ad78f1945e8f9de9e5b995fe4","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2020-05-18T20:09:20Z","title_canon_sha256":"60fc370e386548e8d55c04885cc330ceee3532e206e552dbb7bd5cfcf24dc941"},"schema_version":"1.0","source":{"id":"2005.09056","kind":"arxiv","version":1}},"canonical_sha256":"07588bcc0fbca28a14d065af246c8128c90507010294d52e0a3981cf56cb20bd","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"07588bcc0fbca28a14d065af246c8128c90507010294d52e0a3981cf56cb20bd","first_computed_at":"2026-07-05T01:57:05.852187Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:57:05.852187Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"EV9Uq8kEB2hGgnHzFkbTPOkBRNeRXSvNaYVn9EbULWub9NZh+uLkezWczwUZWwU5K9gC6sqdcxHQIwdf/c8SDw==","signature_status":"signed_v1","signed_at":"2026-07-05T01:57:05.852671Z","signed_message":"canonical_sha256_bytes"},"source_id":"2005.09056","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:38d3aebd80afe2324af58dc4b26e087078d1f654abe70d18496fa1f096c35135","sha256:cb0b48ff241fdfd256ae7ac36d5e479dc52fafaedcd047474743a42f7410305d"],"state_sha256":"0933f837085a243fc1b836d1f344971b37dcdce8f3c7e22ca15bd3dbf212e8fa"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NR5TPJWk5QFrpyMsqwYMYEXHHFXd1SG2dnh9KkkZ+9cgUISkhFlxWDVQdSqz8WON2errcdmUAbhbD8kCTMbJAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T17:47:11.818214Z","bundle_sha256":"ada84f4804d92abda86c9f0a65d5865a5f2fb6fabbf1c66dd84bf7bcbbc5a335"}}