{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:DMHBZHEH5CFXL5VZVI3P4K5ICM","short_pith_number":"pith:DMHBZHEH","canonical_record":{"source":{"id":"1703.02237","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2017-03-07T06:40:44Z","cross_cats_sorted":["stat.ME"],"title_canon_sha256":"c9334711cd3fe749645626b2405d8863594423784962936f70b99127c27926a1","abstract_canon_sha256":"b729e6301b18c8f2d0c0121621a90c734220d2ad9f88c9ad1a2400bbaa18c4ce"},"schema_version":"1.0"},"canonical_sha256":"1b0e1c9c87e88b75f6b9aa36fe2ba8130008717bb998e800022fe8b13b757d0e","source":{"kind":"arxiv","id":"1703.02237","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1703.02237","created_at":"2026-05-18T00:49:20Z"},{"alias_kind":"arxiv_version","alias_value":"1703.02237v1","created_at":"2026-05-18T00:49:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1703.02237","created_at":"2026-05-18T00:49:20Z"},{"alias_kind":"pith_short_12","alias_value":"DMHBZHEH5CFX","created_at":"2026-05-18T12:31:10Z"},{"alias_kind":"pith_short_16","alias_value":"DMHBZHEH5CFXL5VZ","created_at":"2026-05-18T12:31:10Z"},{"alias_kind":"pith_short_8","alias_value":"DMHBZHEH","created_at":"2026-05-18T12:31:10Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:DMHBZHEH5CFXL5VZVI3P4K5ICM","target":"record","payload":{"canonical_record":{"source":{"id":"1703.02237","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2017-03-07T06:40:44Z","cross_cats_sorted":["stat.ME"],"title_canon_sha256":"c9334711cd3fe749645626b2405d8863594423784962936f70b99127c27926a1","abstract_canon_sha256":"b729e6301b18c8f2d0c0121621a90c734220d2ad9f88c9ad1a2400bbaa18c4ce"},"schema_version":"1.0"},"canonical_sha256":"1b0e1c9c87e88b75f6b9aa36fe2ba8130008717bb998e800022fe8b13b757d0e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:49:20.425291Z","signature_b64":"TLTubVVlyUa0I6E2BCIqruh2c/DtqrEvxi3JW//YAIz1oBeLZZ/93WpeQUXcoVwa1r8GQ87et1o3TMhQHd3+Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1b0e1c9c87e88b75f6b9aa36fe2ba8130008717bb998e800022fe8b13b757d0e","last_reissued_at":"2026-05-18T00:49:20.424926Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:49:20.424926Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1703.02237","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":"2026-05-18T00:49:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"uaC+2pxBDWfiyHmrvFCaAgN+1e3YLOnKO5QnwoBJO7WvPbOyCxqadsBVY/eeSSciTuztST1UlhZeIrtB58xcDw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T12:30:26.871847Z"},"content_sha256":"18e9205c8827d0d5d58187a85622ecbcb6c4aa656cfff1e978e130d5cb6d81b2","schema_version":"1.0","event_id":"sha256:18e9205c8827d0d5d58187a85622ecbcb6c4aa656cfff1e978e130d5cb6d81b2"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:DMHBZHEH5CFXL5VZVI3P4K5ICM","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Scalable Collaborative Targeted Learning for High-Dimensional Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ME"],"primary_cat":"stat.CO","authors_text":"Antoine Chambaz, Cheng Ju, Jessica M. Franklin, Mark J. van der Laan, Richard Wyss, Samuel D. Lendle, Sebastian Schneeweiss, Susan Gruber","submitted_at":"2017-03-07T06:40:44Z","abstract_excerpt":"Robust inference of a low-dimensional parameter in a large semi-parametric model relies on external estimators of infinite-dimensional features of the distribution of the data. Typically, only one of the latter is optimized for the sake of constructing a well behaved estimator of the low-dimensional parameter of interest. Optimizing more than one of them for the sake of achieving a better bias-variance trade-off in the estimation of the parameter of interest is the core idea driving the general template of the collaborative targeted minimum loss-based estimation (C-TMLE) procedure. The origina"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1703.02237","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":"2026-05-18T00:49:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"E+7R/kF6gEtFs+p7lDrkjLbQS6hLi5EiUhaQIO8b+J7+Ewll+HmwO70SD//fgJymUA+k47AK1tZxyOWHTmdTAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-28T12:30:26.872192Z"},"content_sha256":"450c3833c46cd1c2ff9735f6f13ad2ee866daf1deb6024b840ff95118cf6b56b","schema_version":"1.0","event_id":"sha256:450c3833c46cd1c2ff9735f6f13ad2ee866daf1deb6024b840ff95118cf6b56b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/DMHBZHEH5CFXL5VZVI3P4K5ICM/bundle.json","state_url":"https://pith.science/pith/DMHBZHEH5CFXL5VZVI3P4K5ICM/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/DMHBZHEH5CFXL5VZVI3P4K5ICM/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":"2026-05-28T12:30:26Z","links":{"resolver":"https://pith.science/pith/DMHBZHEH5CFXL5VZVI3P4K5ICM","bundle":"https://pith.science/pith/DMHBZHEH5CFXL5VZVI3P4K5ICM/bundle.json","state":"https://pith.science/pith/DMHBZHEH5CFXL5VZVI3P4K5ICM/state.json","well_known_bundle":"https://pith.science/.well-known/pith/DMHBZHEH5CFXL5VZVI3P4K5ICM/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:DMHBZHEH5CFXL5VZVI3P4K5ICM","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":"b729e6301b18c8f2d0c0121621a90c734220d2ad9f88c9ad1a2400bbaa18c4ce","cross_cats_sorted":["stat.ME"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2017-03-07T06:40:44Z","title_canon_sha256":"c9334711cd3fe749645626b2405d8863594423784962936f70b99127c27926a1"},"schema_version":"1.0","source":{"id":"1703.02237","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1703.02237","created_at":"2026-05-18T00:49:20Z"},{"alias_kind":"arxiv_version","alias_value":"1703.02237v1","created_at":"2026-05-18T00:49:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1703.02237","created_at":"2026-05-18T00:49:20Z"},{"alias_kind":"pith_short_12","alias_value":"DMHBZHEH5CFX","created_at":"2026-05-18T12:31:10Z"},{"alias_kind":"pith_short_16","alias_value":"DMHBZHEH5CFXL5VZ","created_at":"2026-05-18T12:31:10Z"},{"alias_kind":"pith_short_8","alias_value":"DMHBZHEH","created_at":"2026-05-18T12:31:10Z"}],"graph_snapshots":[{"event_id":"sha256:450c3833c46cd1c2ff9735f6f13ad2ee866daf1deb6024b840ff95118cf6b56b","target":"graph","created_at":"2026-05-18T00:49:20Z","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":"Robust inference of a low-dimensional parameter in a large semi-parametric model relies on external estimators of infinite-dimensional features of the distribution of the data. Typically, only one of the latter is optimized for the sake of constructing a well behaved estimator of the low-dimensional parameter of interest. Optimizing more than one of them for the sake of achieving a better bias-variance trade-off in the estimation of the parameter of interest is the core idea driving the general template of the collaborative targeted minimum loss-based estimation (C-TMLE) procedure. The origina","authors_text":"Antoine Chambaz, Cheng Ju, Jessica M. Franklin, Mark J. van der Laan, Richard Wyss, Samuel D. Lendle, Sebastian Schneeweiss, Susan Gruber","cross_cats":["stat.ME"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2017-03-07T06:40:44Z","title":"Scalable Collaborative Targeted Learning for High-Dimensional Data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1703.02237","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":"sha256:18e9205c8827d0d5d58187a85622ecbcb6c4aa656cfff1e978e130d5cb6d81b2","target":"record","created_at":"2026-05-18T00:49:20Z","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":"b729e6301b18c8f2d0c0121621a90c734220d2ad9f88c9ad1a2400bbaa18c4ce","cross_cats_sorted":["stat.ME"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2017-03-07T06:40:44Z","title_canon_sha256":"c9334711cd3fe749645626b2405d8863594423784962936f70b99127c27926a1"},"schema_version":"1.0","source":{"id":"1703.02237","kind":"arxiv","version":1}},"canonical_sha256":"1b0e1c9c87e88b75f6b9aa36fe2ba8130008717bb998e800022fe8b13b757d0e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1b0e1c9c87e88b75f6b9aa36fe2ba8130008717bb998e800022fe8b13b757d0e","first_computed_at":"2026-05-18T00:49:20.424926Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:49:20.424926Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"TLTubVVlyUa0I6E2BCIqruh2c/DtqrEvxi3JW//YAIz1oBeLZZ/93WpeQUXcoVwa1r8GQ87et1o3TMhQHd3+Dg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:49:20.425291Z","signed_message":"canonical_sha256_bytes"},"source_id":"1703.02237","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:18e9205c8827d0d5d58187a85622ecbcb6c4aa656cfff1e978e130d5cb6d81b2","sha256:450c3833c46cd1c2ff9735f6f13ad2ee866daf1deb6024b840ff95118cf6b56b"],"state_sha256":"df0970eb2c87b3e545a5ea16f0d30ece5fdbf9513f3e760f70551bd22547cdc1"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"STcyhJSMhfnNsVCgmo+0SwWdz6zb/a+MWAhnqGFr0Jl1VJgFM9biWJg8UQMHa/OBp1Qolao/gQoaf3IoWrE7CA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-28T12:30:26.874354Z","bundle_sha256":"d4b9d7172872fb5722c4132059e4955980ea993c69f59d027598136b16bec39e"}}