{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:NQOU4WPNVXMSSTSAOBCJUCWPJP","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":"044eb37fcaf5a5a6428a29a9eec3c16e542c8387501a2a89d3326e3885191c70","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"q-bio.QM","submitted_at":"2021-05-29T18:25:07Z","title_canon_sha256":"496d253517c269baf49f6ca64257f0c8911ebe15eef78f6d455662c8702498d6"},"schema_version":"1.0","source":{"id":"2105.14351","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2105.14351","created_at":"2026-07-05T03:13:49Z"},{"alias_kind":"arxiv_version","alias_value":"2105.14351v1","created_at":"2026-07-05T03:13:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2105.14351","created_at":"2026-07-05T03:13:49Z"},{"alias_kind":"pith_short_12","alias_value":"NQOU4WPNVXMS","created_at":"2026-07-05T03:13:49Z"},{"alias_kind":"pith_short_16","alias_value":"NQOU4WPNVXMSSTSA","created_at":"2026-07-05T03:13:49Z"},{"alias_kind":"pith_short_8","alias_value":"NQOU4WPN","created_at":"2026-07-05T03:13:49Z"}],"graph_snapshots":[{"event_id":"sha256:c5df153b0466cd12c848c9c6b4f6b94d79837bdd9c9617147ca6eebc2cc0ff2c","target":"graph","created_at":"2026-07-05T03:13:49Z","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/2105.14351/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We investigate the predictive performance of two novel CNN-DNN machine learning ensemble models in predicting county-level corn yields across the US Corn Belt (12 states). The developed data set is a combination of management, environment, and historical corn yields from 1980-2019. Two scenarios for ensemble creation are considered: homogenous and heterogeneous ensembles. In homogenous ensembles, the base CNN-DNN models are all the same, but they are generated with a bagging procedure to ensure they exhibit a certain level of diversity. Heterogenous ensembles are created from different base CN","authors_text":"Guiping Hu, Mohsen Shahhosseini, Saeed Khaki, Sotirios V. Archontoulis","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"q-bio.QM","submitted_at":"2021-05-29T18:25:07Z","title":"Corn Yield Prediction with Ensemble CNN-DNN"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2105.14351","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":"sha256:1b380602e2670a53c6f8057b1f723a3fcd7dc3d86947abc71bd52eb14e9638b4","target":"record","created_at":"2026-07-05T03:13:49Z","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":"044eb37fcaf5a5a6428a29a9eec3c16e542c8387501a2a89d3326e3885191c70","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/publicdomain/zero/1.0/","primary_cat":"q-bio.QM","submitted_at":"2021-05-29T18:25:07Z","title_canon_sha256":"496d253517c269baf49f6ca64257f0c8911ebe15eef78f6d455662c8702498d6"},"schema_version":"1.0","source":{"id":"2105.14351","kind":"arxiv","version":1}},"canonical_sha256":"6c1d4e59edadd9294e4070449a0acf4bd454a42b48bf86be94dc098217a7a3e3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6c1d4e59edadd9294e4070449a0acf4bd454a42b48bf86be94dc098217a7a3e3","first_computed_at":"2026-07-05T03:13:49.938908Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:13:49.938908Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"3iI+Ok3AV2O28WzkZlybgmYZxmOWAb0ynQzfvOWnzMuzgq7x+wtBr+3MVYuGmlJR8JUR1CjctCf/w2bo3VOKCA==","signature_status":"signed_v1","signed_at":"2026-07-05T03:13:49.939403Z","signed_message":"canonical_sha256_bytes"},"source_id":"2105.14351","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1b380602e2670a53c6f8057b1f723a3fcd7dc3d86947abc71bd52eb14e9638b4","sha256:c5df153b0466cd12c848c9c6b4f6b94d79837bdd9c9617147ca6eebc2cc0ff2c"],"state_sha256":"ac193ef28a11ba773ade137f8dcf3a8b6827062df63fcd760f0da1654e3f0409"}