{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:IKAEJEWCDVHYEBQ7NIOSU5ZGRE","short_pith_number":"pith:IKAEJEWC","schema_version":"1.0","canonical_sha256":"42804492c21d4f82061f6a1d2a77268910c963936782df70532fce0abd4aedbf","source":{"kind":"arxiv","id":"1805.06879","version":1},"attestation_state":"computed","paper":{"title":"Neural language representations predict outcomes of scientific research","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CY","cs.LG","stat.ML"],"primary_cat":"cs.CL","authors_text":"Daniel Berenberg, James P. Bagrow, Joshua Bongard","submitted_at":"2018-05-17T17:40:12Z","abstract_excerpt":"Many research fields codify their findings in standard formats, often by reporting correlations between quantities of interest. But the space of all testable correlates is far larger than scientific resources can currently address, so the ability to accurately predict correlations would be useful to plan research and allocate resources. Using a dataset of approximately 170,000 correlational findings extracted from leading social science journals, we show that a trained neural network can accurately predict the reported correlations using only the text descriptions of the correlates. Accurate p"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1805.06879","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2018-05-17T17:40:12Z","cross_cats_sorted":["cs.AI","cs.CY","cs.LG","stat.ML"],"title_canon_sha256":"9f68e2c4e724c3b5141ef5386c7e2299a8c1f3f257f3f8c69f4cbf7c1d6998a3","abstract_canon_sha256":"4f23ff9ec5cb58867d52c87dfecb2335667570f34ef178b68814f27b5a53779b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:15:43.039948Z","signature_b64":"bZe4gfnUDjFch3UIH/eIlZ+412rUyk9KXO6AX6SlePEy+k1lu7bgVKel6hJrvPCIDhWlqE/cqO46KTdY22lADQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"42804492c21d4f82061f6a1d2a77268910c963936782df70532fce0abd4aedbf","last_reissued_at":"2026-05-18T00:15:43.039432Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:15:43.039432Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Neural language representations predict outcomes of scientific research","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CY","cs.LG","stat.ML"],"primary_cat":"cs.CL","authors_text":"Daniel Berenberg, James P. Bagrow, Joshua Bongard","submitted_at":"2018-05-17T17:40:12Z","abstract_excerpt":"Many research fields codify their findings in standard formats, often by reporting correlations between quantities of interest. But the space of all testable correlates is far larger than scientific resources can currently address, so the ability to accurately predict correlations would be useful to plan research and allocate resources. Using a dataset of approximately 170,000 correlational findings extracted from leading social science journals, we show that a trained neural network can accurately predict the reported correlations using only the text descriptions of the correlates. Accurate p"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.06879","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1805.06879","created_at":"2026-05-18T00:15:43.039509+00:00"},{"alias_kind":"arxiv_version","alias_value":"1805.06879v1","created_at":"2026-05-18T00:15:43.039509+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.06879","created_at":"2026-05-18T00:15:43.039509+00:00"},{"alias_kind":"pith_short_12","alias_value":"IKAEJEWCDVHY","created_at":"2026-05-18T12:32:31.084164+00:00"},{"alias_kind":"pith_short_16","alias_value":"IKAEJEWCDVHYEBQ7","created_at":"2026-05-18T12:32:31.084164+00:00"},{"alias_kind":"pith_short_8","alias_value":"IKAEJEWC","created_at":"2026-05-18T12:32:31.084164+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/IKAEJEWCDVHYEBQ7NIOSU5ZGRE","json":"https://pith.science/pith/IKAEJEWCDVHYEBQ7NIOSU5ZGRE.json","graph_json":"https://pith.science/api/pith-number/IKAEJEWCDVHYEBQ7NIOSU5ZGRE/graph.json","events_json":"https://pith.science/api/pith-number/IKAEJEWCDVHYEBQ7NIOSU5ZGRE/events.json","paper":"https://pith.science/paper/IKAEJEWC"},"agent_actions":{"view_html":"https://pith.science/pith/IKAEJEWCDVHYEBQ7NIOSU5ZGRE","download_json":"https://pith.science/pith/IKAEJEWCDVHYEBQ7NIOSU5ZGRE.json","view_paper":"https://pith.science/paper/IKAEJEWC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1805.06879&json=true","fetch_graph":"https://pith.science/api/pith-number/IKAEJEWCDVHYEBQ7NIOSU5ZGRE/graph.json","fetch_events":"https://pith.science/api/pith-number/IKAEJEWCDVHYEBQ7NIOSU5ZGRE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IKAEJEWCDVHYEBQ7NIOSU5ZGRE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IKAEJEWCDVHYEBQ7NIOSU5ZGRE/action/storage_attestation","attest_author":"https://pith.science/pith/IKAEJEWCDVHYEBQ7NIOSU5ZGRE/action/author_attestation","sign_citation":"https://pith.science/pith/IKAEJEWCDVHYEBQ7NIOSU5ZGRE/action/citation_signature","submit_replication":"https://pith.science/pith/IKAEJEWCDVHYEBQ7NIOSU5ZGRE/action/replication_record"}},"created_at":"2026-05-18T00:15:43.039509+00:00","updated_at":"2026-05-18T00:15:43.039509+00:00"}