{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:U5UTZE43VNVGYHK2DGKW3PSDBV","short_pith_number":"pith:U5UTZE43","canonical_record":{"source":{"id":"1712.05483","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-12-15T00:12:47Z","cross_cats_sorted":[],"title_canon_sha256":"663fd028a5cd3f990e4008f7a76887aa977c6825578791b6abf36d1e7de3e4c0","abstract_canon_sha256":"a1461910bcd6dd0b72d93d2439c81c07e6b5858d02536cb63ff4c97a5f448bdb"},"schema_version":"1.0"},"canonical_sha256":"a7693c939bab6a6c1d5a19956dbe430d70078c96ae4bf64e8944b21d3f82e4fe","source":{"kind":"arxiv","id":"1712.05483","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1712.05483","created_at":"2026-05-18T00:27:56Z"},{"alias_kind":"arxiv_version","alias_value":"1712.05483v1","created_at":"2026-05-18T00:27:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1712.05483","created_at":"2026-05-18T00:27:56Z"},{"alias_kind":"pith_short_12","alias_value":"U5UTZE43VNVG","created_at":"2026-05-18T12:31:46Z"},{"alias_kind":"pith_short_16","alias_value":"U5UTZE43VNVGYHK2","created_at":"2026-05-18T12:31:46Z"},{"alias_kind":"pith_short_8","alias_value":"U5UTZE43","created_at":"2026-05-18T12:31:46Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:U5UTZE43VNVGYHK2DGKW3PSDBV","target":"record","payload":{"canonical_record":{"source":{"id":"1712.05483","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-12-15T00:12:47Z","cross_cats_sorted":[],"title_canon_sha256":"663fd028a5cd3f990e4008f7a76887aa977c6825578791b6abf36d1e7de3e4c0","abstract_canon_sha256":"a1461910bcd6dd0b72d93d2439c81c07e6b5858d02536cb63ff4c97a5f448bdb"},"schema_version":"1.0"},"canonical_sha256":"a7693c939bab6a6c1d5a19956dbe430d70078c96ae4bf64e8944b21d3f82e4fe","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:27:56.304790Z","signature_b64":"SIfCh4Qp5WeTTfiZXBhos32DP+RV0tXCo9g+/zcN1dxSKkc1hISU3b4Bl0zXIOTAGIIZQuXabKQ3tBOwApRBAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a7693c939bab6a6c1d5a19956dbe430d70078c96ae4bf64e8944b21d3f82e4fe","last_reissued_at":"2026-05-18T00:27:56.304100Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:27:56.304100Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1712.05483","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":"2026-05-18T00:27:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ga607sfUEt9B6cHmFWNCmHk6zYt1HJ7E/6chwsceaRTBR7A0yVxD0F8g+9Hc2UHPcZ56sk8c0ddtwYOKf73WCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T17:35:17.173892Z"},"content_sha256":"aecce1a8971e522e84323da51392709000c45ff3bc0e1f22670895d377bccf74","schema_version":"1.0","event_id":"sha256:aecce1a8971e522e84323da51392709000c45ff3bc0e1f22670895d377bccf74"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:U5UTZE43VNVGYHK2DGKW3PSDBV","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Learning when to skim and when to read","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Alexander Rosenberg Johansen, Richard Socher","submitted_at":"2017-12-15T00:12:47Z","abstract_excerpt":"Many recent advances in deep learning for natural language processing have come at increasing computational cost, but the power of these state-of-the-art models is not needed for every example in a dataset. We demonstrate two approaches to reducing unnecessary computation in cases where a fast but weak baseline classier and a stronger, slower model are both available. Applying an AUC-based metric to the task of sentiment classification, we find significant efficiency gains with both a probability-threshold method for reducing computational cost and one that uses a secondary decision network."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1712.05483","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":"2026-05-18T00:27:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lfLfuOM7Faf2ZF/WbRbltGPLupZ1byeEsyaR8jiBgEaFMEZv86da5IV8yHe8gX083PIXbf+y6Ahso1JPsTdsAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-27T17:35:17.174337Z"},"content_sha256":"fa4f9c164218e9ba2ece4153fe10abd7d33f1ea083bbb7b9334f2869ef1ee529","schema_version":"1.0","event_id":"sha256:fa4f9c164218e9ba2ece4153fe10abd7d33f1ea083bbb7b9334f2869ef1ee529"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/U5UTZE43VNVGYHK2DGKW3PSDBV/bundle.json","state_url":"https://pith.science/pith/U5UTZE43VNVGYHK2DGKW3PSDBV/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/U5UTZE43VNVGYHK2DGKW3PSDBV/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":"2026-05-27T17:35:17Z","links":{"resolver":"https://pith.science/pith/U5UTZE43VNVGYHK2DGKW3PSDBV","bundle":"https://pith.science/pith/U5UTZE43VNVGYHK2DGKW3PSDBV/bundle.json","state":"https://pith.science/pith/U5UTZE43VNVGYHK2DGKW3PSDBV/state.json","well_known_bundle":"https://pith.science/.well-known/pith/U5UTZE43VNVGYHK2DGKW3PSDBV/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:U5UTZE43VNVGYHK2DGKW3PSDBV","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":"a1461910bcd6dd0b72d93d2439c81c07e6b5858d02536cb63ff4c97a5f448bdb","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-12-15T00:12:47Z","title_canon_sha256":"663fd028a5cd3f990e4008f7a76887aa977c6825578791b6abf36d1e7de3e4c0"},"schema_version":"1.0","source":{"id":"1712.05483","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1712.05483","created_at":"2026-05-18T00:27:56Z"},{"alias_kind":"arxiv_version","alias_value":"1712.05483v1","created_at":"2026-05-18T00:27:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1712.05483","created_at":"2026-05-18T00:27:56Z"},{"alias_kind":"pith_short_12","alias_value":"U5UTZE43VNVG","created_at":"2026-05-18T12:31:46Z"},{"alias_kind":"pith_short_16","alias_value":"U5UTZE43VNVGYHK2","created_at":"2026-05-18T12:31:46Z"},{"alias_kind":"pith_short_8","alias_value":"U5UTZE43","created_at":"2026-05-18T12:31:46Z"}],"graph_snapshots":[{"event_id":"sha256:fa4f9c164218e9ba2ece4153fe10abd7d33f1ea083bbb7b9334f2869ef1ee529","target":"graph","created_at":"2026-05-18T00:27:56Z","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":"Many recent advances in deep learning for natural language processing have come at increasing computational cost, but the power of these state-of-the-art models is not needed for every example in a dataset. We demonstrate two approaches to reducing unnecessary computation in cases where a fast but weak baseline classier and a stronger, slower model are both available. Applying an AUC-based metric to the task of sentiment classification, we find significant efficiency gains with both a probability-threshold method for reducing computational cost and one that uses a secondary decision network.","authors_text":"Alexander Rosenberg Johansen, Richard Socher","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-12-15T00:12:47Z","title":"Learning when to skim and when to read"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1712.05483","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:aecce1a8971e522e84323da51392709000c45ff3bc0e1f22670895d377bccf74","target":"record","created_at":"2026-05-18T00:27:56Z","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":"a1461910bcd6dd0b72d93d2439c81c07e6b5858d02536cb63ff4c97a5f448bdb","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2017-12-15T00:12:47Z","title_canon_sha256":"663fd028a5cd3f990e4008f7a76887aa977c6825578791b6abf36d1e7de3e4c0"},"schema_version":"1.0","source":{"id":"1712.05483","kind":"arxiv","version":1}},"canonical_sha256":"a7693c939bab6a6c1d5a19956dbe430d70078c96ae4bf64e8944b21d3f82e4fe","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a7693c939bab6a6c1d5a19956dbe430d70078c96ae4bf64e8944b21d3f82e4fe","first_computed_at":"2026-05-18T00:27:56.304100Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:27:56.304100Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"SIfCh4Qp5WeTTfiZXBhos32DP+RV0tXCo9g+/zcN1dxSKkc1hISU3b4Bl0zXIOTAGIIZQuXabKQ3tBOwApRBAQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:27:56.304790Z","signed_message":"canonical_sha256_bytes"},"source_id":"1712.05483","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:aecce1a8971e522e84323da51392709000c45ff3bc0e1f22670895d377bccf74","sha256:fa4f9c164218e9ba2ece4153fe10abd7d33f1ea083bbb7b9334f2869ef1ee529"],"state_sha256":"92b4da28932c851beb3c06085d334c5fe3b6b9635c9b4e91d7c8d3ce281941df"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"StyS2jpCUq7WZ4VmjYEQjZiLo3tGge9AzlskxqsnXkwft42vTnK8vzcM7Ub2hZP3d9cgV2YJXIYBhthQPf6MDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-27T17:35:17.176639Z","bundle_sha256":"2e9af0673c2c72d62c875093fc1686f12bece3468c7e9d5a1e51e358a568dd82"}}