{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:IDF3GXZNQLVOZKNPR55XXCKWU7","short_pith_number":"pith:IDF3GXZN","canonical_record":{"source":{"id":"1907.01463","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-07-02T15:46:46Z","cross_cats_sorted":["cs.CY","stat.ML"],"title_canon_sha256":"e488c5a07777f7bd3a79365de969ff8c973cdd69eed40d0d25cf5b31e6354ed8","abstract_canon_sha256":"be680bc6297b89074bf8f3b20f31ed6bd6e5e023ffac27885c44a27dce562bee"},"schema_version":"1.0"},"canonical_sha256":"40cbb35f2d82eaeca9af8f7b7b8956a7c7c206836bd6f06fb16e7eb002ec96f8","source":{"kind":"arxiv","id":"1907.01463","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1907.01463","created_at":"2026-05-17T23:41:40Z"},{"alias_kind":"arxiv_version","alias_value":"1907.01463v1","created_at":"2026-05-17T23:41:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.01463","created_at":"2026-05-17T23:41:40Z"},{"alias_kind":"pith_short_12","alias_value":"IDF3GXZNQLVO","created_at":"2026-05-18T12:33:18Z"},{"alias_kind":"pith_short_16","alias_value":"IDF3GXZNQLVOZKNP","created_at":"2026-05-18T12:33:18Z"},{"alias_kind":"pith_short_8","alias_value":"IDF3GXZN","created_at":"2026-05-18T12:33:18Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:IDF3GXZNQLVOZKNPR55XXCKWU7","target":"record","payload":{"canonical_record":{"source":{"id":"1907.01463","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-07-02T15:46:46Z","cross_cats_sorted":["cs.CY","stat.ML"],"title_canon_sha256":"e488c5a07777f7bd3a79365de969ff8c973cdd69eed40d0d25cf5b31e6354ed8","abstract_canon_sha256":"be680bc6297b89074bf8f3b20f31ed6bd6e5e023ffac27885c44a27dce562bee"},"schema_version":"1.0"},"canonical_sha256":"40cbb35f2d82eaeca9af8f7b7b8956a7c7c206836bd6f06fb16e7eb002ec96f8","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:41:40.329308Z","signature_b64":"OMpHN3RdGkgMUkEOVXlnntAXMN0n7U8SL0Sdl/IFnPXWXH5OqdvH/o5ttCTync1h3WIkOwCpEbYBLR70s9XVBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"40cbb35f2d82eaeca9af8f7b7b8956a7c7c206836bd6f06fb16e7eb002ec96f8","last_reissued_at":"2026-05-17T23:41:40.328737Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:41:40.328737Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1907.01463","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":"2026-05-17T23:41:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"0lmglUjkhd/DSJGMCkW9yDuh+NjmG6AKWaXvf0kpn7DpMKIg2WtZjq5yLKVx65TDnRIvtb+zpl8VSyCV8T2qAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T08:30:37.482256Z"},"content_sha256":"e38b5b0a037b7cc42ac60a1a1bed97b018a067de7cfac5fd724ac021bc842207","schema_version":"1.0","event_id":"sha256:e38b5b0a037b7cc42ac60a1a1bed97b018a067de7cfac5fd724ac021bc842207"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:IDF3GXZNQLVOZKNPR55XXCKWU7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Reproducibility in Machine Learning for Health","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CY","stat.ML"],"primary_cat":"cs.LG","authors_text":"(2) University of Toronto, (3) Evidation Health, (4) New York University, 5), (5) Vector Institute), Inc., Luca Foschini (3) ((1) Massachusetts Institute of Technology, Marzyeh Ghassemi (2, Matthew B.A. McDermott (1), Nikki Marinsek (3), Rajesh Ranganath (4), Shirly Wang (2)","submitted_at":"2019-07-02T15:46:46Z","abstract_excerpt":"Machine learning algorithms designed to characterize, monitor, and intervene on human health (ML4H) are expected to perform safely and reliably when operating at scale, potentially outside strict human supervision. This requirement warrants a stricter attention to issues of reproducibility than other fields of machine learning.\n  In this work, we conduct a systematic evaluation of over 100 recently published ML4H research papers along several dimensions related to reproducibility. We find that the field of ML4H compares poorly to more established machine learning fields, particularly concernin"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.01463","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":"2026-05-17T23:41:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9VEbG3coUtrCIaG4fPBt3pUDjsBdTk37PEQQPm8gCYd91IHuPqzPES7ZpThjcEnJWaW/+ey3t6IGhoiFoptdDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T08:30:37.482906Z"},"content_sha256":"305dce50baf8e40e0590b8306c3153c1096dcc593cbbac4ce2934338584357fd","schema_version":"1.0","event_id":"sha256:305dce50baf8e40e0590b8306c3153c1096dcc593cbbac4ce2934338584357fd"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/IDF3GXZNQLVOZKNPR55XXCKWU7/bundle.json","state_url":"https://pith.science/pith/IDF3GXZNQLVOZKNPR55XXCKWU7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/IDF3GXZNQLVOZKNPR55XXCKWU7/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":"2026-05-27T08:30:37Z","links":{"resolver":"https://pith.science/pith/IDF3GXZNQLVOZKNPR55XXCKWU7","bundle":"https://pith.science/pith/IDF3GXZNQLVOZKNPR55XXCKWU7/bundle.json","state":"https://pith.science/pith/IDF3GXZNQLVOZKNPR55XXCKWU7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/IDF3GXZNQLVOZKNPR55XXCKWU7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:IDF3GXZNQLVOZKNPR55XXCKWU7","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":"be680bc6297b89074bf8f3b20f31ed6bd6e5e023ffac27885c44a27dce562bee","cross_cats_sorted":["cs.CY","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-07-02T15:46:46Z","title_canon_sha256":"e488c5a07777f7bd3a79365de969ff8c973cdd69eed40d0d25cf5b31e6354ed8"},"schema_version":"1.0","source":{"id":"1907.01463","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1907.01463","created_at":"2026-05-17T23:41:40Z"},{"alias_kind":"arxiv_version","alias_value":"1907.01463v1","created_at":"2026-05-17T23:41:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.01463","created_at":"2026-05-17T23:41:40Z"},{"alias_kind":"pith_short_12","alias_value":"IDF3GXZNQLVO","created_at":"2026-05-18T12:33:18Z"},{"alias_kind":"pith_short_16","alias_value":"IDF3GXZNQLVOZKNP","created_at":"2026-05-18T12:33:18Z"},{"alias_kind":"pith_short_8","alias_value":"IDF3GXZN","created_at":"2026-05-18T12:33:18Z"}],"graph_snapshots":[{"event_id":"sha256:305dce50baf8e40e0590b8306c3153c1096dcc593cbbac4ce2934338584357fd","target":"graph","created_at":"2026-05-17T23:41:40Z","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":"Machine learning algorithms designed to characterize, monitor, and intervene on human health (ML4H) are expected to perform safely and reliably when operating at scale, potentially outside strict human supervision. This requirement warrants a stricter attention to issues of reproducibility than other fields of machine learning.\n  In this work, we conduct a systematic evaluation of over 100 recently published ML4H research papers along several dimensions related to reproducibility. We find that the field of ML4H compares poorly to more established machine learning fields, particularly concernin","authors_text":"(2) University of Toronto, (3) Evidation Health, (4) New York University, 5), (5) Vector Institute), Inc., Luca Foschini (3) ((1) Massachusetts Institute of Technology, Marzyeh Ghassemi (2, Matthew B.A. McDermott (1), Nikki Marinsek (3), Rajesh Ranganath (4), Shirly Wang (2)","cross_cats":["cs.CY","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-07-02T15:46:46Z","title":"Reproducibility in Machine Learning for Health"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.01463","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:e38b5b0a037b7cc42ac60a1a1bed97b018a067de7cfac5fd724ac021bc842207","target":"record","created_at":"2026-05-17T23:41:40Z","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":"be680bc6297b89074bf8f3b20f31ed6bd6e5e023ffac27885c44a27dce562bee","cross_cats_sorted":["cs.CY","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-07-02T15:46:46Z","title_canon_sha256":"e488c5a07777f7bd3a79365de969ff8c973cdd69eed40d0d25cf5b31e6354ed8"},"schema_version":"1.0","source":{"id":"1907.01463","kind":"arxiv","version":1}},"canonical_sha256":"40cbb35f2d82eaeca9af8f7b7b8956a7c7c206836bd6f06fb16e7eb002ec96f8","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"40cbb35f2d82eaeca9af8f7b7b8956a7c7c206836bd6f06fb16e7eb002ec96f8","first_computed_at":"2026-05-17T23:41:40.328737Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:41:40.328737Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"OMpHN3RdGkgMUkEOVXlnntAXMN0n7U8SL0Sdl/IFnPXWXH5OqdvH/o5ttCTync1h3WIkOwCpEbYBLR70s9XVBw==","signature_status":"signed_v1","signed_at":"2026-05-17T23:41:40.329308Z","signed_message":"canonical_sha256_bytes"},"source_id":"1907.01463","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e38b5b0a037b7cc42ac60a1a1bed97b018a067de7cfac5fd724ac021bc842207","sha256:305dce50baf8e40e0590b8306c3153c1096dcc593cbbac4ce2934338584357fd"],"state_sha256":"06a73d765b19480a6fab211ea51e0e567a5c181e7014efef180f244e89ef354e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"isHVY/hv9ncNH1Iem6Q4AqErwHPqn6C5q3qoMlc2f8YEHY1F2BBRpGoCZ6dRAXV5oyiWHPfwqwYmEmkYFwcRDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-27T08:30:37.486411Z","bundle_sha256":"d46c1c89b1e8602e84b4c068be3cf434da801718d01f2c9856f0ced840f8f246"}}