{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:YOLOTBGO6PRRYJMPDNSQE2AJGC","short_pith_number":"pith:YOLOTBGO","canonical_record":{"source":{"id":"1805.12044","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-05-30T15:48:05Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"d3db7d05bbc7e2752726869dbbd84ca1115aa82e4dd29332d15b95b59599cec0","abstract_canon_sha256":"4b168845161b3d01893931eb637c2dc29a71e229a4075712fc38c36424be3791"},"schema_version":"1.0"},"canonical_sha256":"c396e984cef3e31c258f1b650268093088263e78f87cf2b404c3ab9be8ceb1b5","source":{"kind":"arxiv","id":"1805.12044","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1805.12044","created_at":"2026-05-18T00:14:35Z"},{"alias_kind":"arxiv_version","alias_value":"1805.12044v1","created_at":"2026-05-18T00:14:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.12044","created_at":"2026-05-18T00:14:35Z"},{"alias_kind":"pith_short_12","alias_value":"YOLOTBGO6PRR","created_at":"2026-05-18T12:33:04Z"},{"alias_kind":"pith_short_16","alias_value":"YOLOTBGO6PRRYJMP","created_at":"2026-05-18T12:33:04Z"},{"alias_kind":"pith_short_8","alias_value":"YOLOTBGO","created_at":"2026-05-18T12:33:04Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:YOLOTBGO6PRRYJMPDNSQE2AJGC","target":"record","payload":{"canonical_record":{"source":{"id":"1805.12044","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-05-30T15:48:05Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"d3db7d05bbc7e2752726869dbbd84ca1115aa82e4dd29332d15b95b59599cec0","abstract_canon_sha256":"4b168845161b3d01893931eb637c2dc29a71e229a4075712fc38c36424be3791"},"schema_version":"1.0"},"canonical_sha256":"c396e984cef3e31c258f1b650268093088263e78f87cf2b404c3ab9be8ceb1b5","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:14:35.429229Z","signature_b64":"HLEQloLqq5p1rx5eYEspfIihV5ZnCJJ1IO11/EI/2WN5HeHYoOAvN9dJ8BdRaP3HFZqia7HV7junYBGa3+9tCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c396e984cef3e31c258f1b650268093088263e78f87cf2b404c3ab9be8ceb1b5","last_reissued_at":"2026-05-18T00:14:35.428523Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:14:35.428523Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1805.12044","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":"2026-05-18T00:14:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Qof4d1ML0pznBgNw7YGaijZgBnlfjEpnUUZL6mQMmSGzDWFvV6CFEvitclPQtl5JgLcdc9Nd64Wq2c1ROe3gDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-08T18:58:32.082999Z"},"content_sha256":"0a03e5508adff4c06d5a9b453ae5e73fd4ca575ca15c43d531b96ba75904e256","schema_version":"1.0","event_id":"sha256:0a03e5508adff4c06d5a9b453ae5e73fd4ca575ca15c43d531b96ba75904e256"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:YOLOTBGO6PRRYJMPDNSQE2AJGC","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Predicting County Level Corn Yields Using Deep Long Short Term Memory Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Baskar Ganapathysubramanian, Chao Liu, Dermot J. Hayes, Nathan P. Hendricks, Soumik Sarkar, Zehui Jiang","submitted_at":"2018-05-30T15:48:05Z","abstract_excerpt":"Corn yield prediction is beneficial as it provides valuable information about production and prices prior the harvest. Publicly available high-quality corn yield prediction can help address emergent information asymmetry problems and in doing so improve price efficiency in futures markets. This paper is the first to employ Long Short-Term Memory (LSTM), a special form of Recurrent Neural Network (RNN) method to predict corn yields. A cross sectional time series of county-level corn yield and hourly weather data made the sample space large enough to use deep learning technics. LSTM is efficient"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.12044","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":"2026-05-18T00:14:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"X5IO6QC7GdoYZcm/L763Un4Pf+wklqNzKhIo+u9YjKEPNTpiq9nrxRNuU+2k+szm5aIPhRZ4ezLu1Yq8C2BQDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-08T18:58:32.083346Z"},"content_sha256":"f764cf5ff3d7a873cc8ff685e763960ca90873e7056d6f7454a1d1273541688a","schema_version":"1.0","event_id":"sha256:f764cf5ff3d7a873cc8ff685e763960ca90873e7056d6f7454a1d1273541688a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/YOLOTBGO6PRRYJMPDNSQE2AJGC/bundle.json","state_url":"https://pith.science/pith/YOLOTBGO6PRRYJMPDNSQE2AJGC/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/YOLOTBGO6PRRYJMPDNSQE2AJGC/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":"2026-06-08T18:58:32Z","links":{"resolver":"https://pith.science/pith/YOLOTBGO6PRRYJMPDNSQE2AJGC","bundle":"https://pith.science/pith/YOLOTBGO6PRRYJMPDNSQE2AJGC/bundle.json","state":"https://pith.science/pith/YOLOTBGO6PRRYJMPDNSQE2AJGC/state.json","well_known_bundle":"https://pith.science/.well-known/pith/YOLOTBGO6PRRYJMPDNSQE2AJGC/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:YOLOTBGO6PRRYJMPDNSQE2AJGC","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":"4b168845161b3d01893931eb637c2dc29a71e229a4075712fc38c36424be3791","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-05-30T15:48:05Z","title_canon_sha256":"d3db7d05bbc7e2752726869dbbd84ca1115aa82e4dd29332d15b95b59599cec0"},"schema_version":"1.0","source":{"id":"1805.12044","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1805.12044","created_at":"2026-05-18T00:14:35Z"},{"alias_kind":"arxiv_version","alias_value":"1805.12044v1","created_at":"2026-05-18T00:14:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.12044","created_at":"2026-05-18T00:14:35Z"},{"alias_kind":"pith_short_12","alias_value":"YOLOTBGO6PRR","created_at":"2026-05-18T12:33:04Z"},{"alias_kind":"pith_short_16","alias_value":"YOLOTBGO6PRRYJMP","created_at":"2026-05-18T12:33:04Z"},{"alias_kind":"pith_short_8","alias_value":"YOLOTBGO","created_at":"2026-05-18T12:33:04Z"}],"graph_snapshots":[{"event_id":"sha256:f764cf5ff3d7a873cc8ff685e763960ca90873e7056d6f7454a1d1273541688a","target":"graph","created_at":"2026-05-18T00:14:35Z","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":"Corn yield prediction is beneficial as it provides valuable information about production and prices prior the harvest. Publicly available high-quality corn yield prediction can help address emergent information asymmetry problems and in doing so improve price efficiency in futures markets. This paper is the first to employ Long Short-Term Memory (LSTM), a special form of Recurrent Neural Network (RNN) method to predict corn yields. A cross sectional time series of county-level corn yield and hourly weather data made the sample space large enough to use deep learning technics. LSTM is efficient","authors_text":"Baskar Ganapathysubramanian, Chao Liu, Dermot J. Hayes, Nathan P. Hendricks, Soumik Sarkar, Zehui Jiang","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-05-30T15:48:05Z","title":"Predicting County Level Corn Yields Using Deep Long Short Term Memory Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.12044","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":"sha256:0a03e5508adff4c06d5a9b453ae5e73fd4ca575ca15c43d531b96ba75904e256","target":"record","created_at":"2026-05-18T00:14:35Z","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":"4b168845161b3d01893931eb637c2dc29a71e229a4075712fc38c36424be3791","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-05-30T15:48:05Z","title_canon_sha256":"d3db7d05bbc7e2752726869dbbd84ca1115aa82e4dd29332d15b95b59599cec0"},"schema_version":"1.0","source":{"id":"1805.12044","kind":"arxiv","version":1}},"canonical_sha256":"c396e984cef3e31c258f1b650268093088263e78f87cf2b404c3ab9be8ceb1b5","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c396e984cef3e31c258f1b650268093088263e78f87cf2b404c3ab9be8ceb1b5","first_computed_at":"2026-05-18T00:14:35.428523Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:14:35.428523Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"HLEQloLqq5p1rx5eYEspfIihV5ZnCJJ1IO11/EI/2WN5HeHYoOAvN9dJ8BdRaP3HFZqia7HV7junYBGa3+9tCQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:14:35.429229Z","signed_message":"canonical_sha256_bytes"},"source_id":"1805.12044","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0a03e5508adff4c06d5a9b453ae5e73fd4ca575ca15c43d531b96ba75904e256","sha256:f764cf5ff3d7a873cc8ff685e763960ca90873e7056d6f7454a1d1273541688a"],"state_sha256":"be99aa2871a113965cb54dc799fd08b2aff7894b1834fd99e9d79902d5a44cb2"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PWVbfCErZrIVITBu8s2ojlV+XecKy7P7+e7sQUYRx9KHVwmRyqbc3Gm+r7v0ASx1BTRhHmTpJqiebZwZknvIBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-08T18:58:32.086472Z","bundle_sha256":"fcf9325108353544ac89a60894d70ab322b63e771424239b1ff3d63d8a2f8a02"}}