{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:HCXX5C6VZG7LVA524RRZ6V2A6I","short_pith_number":"pith:HCXX5C6V","canonical_record":{"source":{"id":"2001.09055","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2020-01-18T03:55:20Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"5e4991ae0f0fda2d213dffae112a2fbe5dc4e33a17988c2a2c8e5e64fb9eb351","abstract_canon_sha256":"6da11dd07fc02969c7f0f5e1651201794f6cc6c43957e305dfe11f40fe31e1c5"},"schema_version":"1.0"},"canonical_sha256":"38af7e8bd5c9beba83bae4639f5740f23d274d069d3a8a36c21454774fc7c5c4","source":{"kind":"arxiv","id":"2001.09055","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2001.09055","created_at":"2026-07-05T01:49:35Z"},{"alias_kind":"arxiv_version","alias_value":"2001.09055v2","created_at":"2026-07-05T01:49:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2001.09055","created_at":"2026-07-05T01:49:35Z"},{"alias_kind":"pith_short_12","alias_value":"HCXX5C6VZG7L","created_at":"2026-07-05T01:49:35Z"},{"alias_kind":"pith_short_16","alias_value":"HCXX5C6VZG7LVA52","created_at":"2026-07-05T01:49:35Z"},{"alias_kind":"pith_short_8","alias_value":"HCXX5C6V","created_at":"2026-07-05T01:49:35Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:HCXX5C6VZG7LVA524RRZ6V2A6I","target":"record","payload":{"canonical_record":{"source":{"id":"2001.09055","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2020-01-18T03:55:20Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"5e4991ae0f0fda2d213dffae112a2fbe5dc4e33a17988c2a2c8e5e64fb9eb351","abstract_canon_sha256":"6da11dd07fc02969c7f0f5e1651201794f6cc6c43957e305dfe11f40fe31e1c5"},"schema_version":"1.0"},"canonical_sha256":"38af7e8bd5c9beba83bae4639f5740f23d274d069d3a8a36c21454774fc7c5c4","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:49:35.791184Z","signature_b64":"hZijq77pV45/1uWaunUAjfLGI5IgY/++BbSw2XlljiK6fZkTtoBY5/n464hY1g6PUhNGjHH6l0MpUOTCRGbjAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"38af7e8bd5c9beba83bae4639f5740f23d274d069d3a8a36c21454774fc7c5c4","last_reissued_at":"2026-07-05T01:49:35.790627Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:49:35.790627Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2001.09055","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T01:49:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"feKAYfQxvRMNDlfThiY3p+GBimL8ibD45hyiF/uXQ3gqYNOyE6mDvmOddMy6yhy36TIIuHQZDBvFpQNvHyfCAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-23T00:36:16.932126Z"},"content_sha256":"57a41284f26337761403c28d2d3ba5f04198bcd8b55972becf22edc3ca8220d7","schema_version":"1.0","event_id":"sha256:57a41284f26337761403c28d2d3ba5f04198bcd8b55972becf22edc3ca8220d7"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:HCXX5C6VZG7LVA524RRZ6V2A6I","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Forecasting Corn Yield with Machine Learning Ensembles","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"stat.AP","authors_text":"Guiping Hu, Mohsen Shahhosseini, Sotirios V. Archontoulis","submitted_at":"2020-01-18T03:55:20Z","abstract_excerpt":"The emerge of new technologies to synthesize and analyze big data with high-performance computing, has increased our capacity to more accurately predict crop yields. Recent research has shown that Machine learning (ML) can provide reasonable predictions, faster, and with higher flexibility compared to simulation crop modeling. The earlier the prediction during the growing season the better, but this has not been thoroughly investigated as previous studies considered all data available to predict yields. This paper provides a machine learning based framework to forecast corn yields in three US "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2001.09055","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2001.09055/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2026-07-05T01:49:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SjEjNWxYjttO/1L6NCJ7H2/nEwm6a3EqwhwGrc4u6j9rNxKpj4kBApR/2RfR5thk5C8BUNIbQ0mMUzY3m3QoCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-23T00:36:16.932749Z"},"content_sha256":"f967caea50ebcdae17015b9949cedef31df51d8e8be3c7260ea85b36a67d209b","schema_version":"1.0","event_id":"sha256:f967caea50ebcdae17015b9949cedef31df51d8e8be3c7260ea85b36a67d209b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/HCXX5C6VZG7LVA524RRZ6V2A6I/bundle.json","state_url":"https://pith.science/pith/HCXX5C6VZG7LVA524RRZ6V2A6I/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/HCXX5C6VZG7LVA524RRZ6V2A6I/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":"2026-08-23T00:36:16Z","links":{"resolver":"https://pith.science/pith/HCXX5C6VZG7LVA524RRZ6V2A6I","bundle":"https://pith.science/pith/HCXX5C6VZG7LVA524RRZ6V2A6I/bundle.json","state":"https://pith.science/pith/HCXX5C6VZG7LVA524RRZ6V2A6I/state.json","well_known_bundle":"https://pith.science/.well-known/pith/HCXX5C6VZG7LVA524RRZ6V2A6I/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:HCXX5C6VZG7LVA524RRZ6V2A6I","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":"6da11dd07fc02969c7f0f5e1651201794f6cc6c43957e305dfe11f40fe31e1c5","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2020-01-18T03:55:20Z","title_canon_sha256":"5e4991ae0f0fda2d213dffae112a2fbe5dc4e33a17988c2a2c8e5e64fb9eb351"},"schema_version":"1.0","source":{"id":"2001.09055","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2001.09055","created_at":"2026-07-05T01:49:35Z"},{"alias_kind":"arxiv_version","alias_value":"2001.09055v2","created_at":"2026-07-05T01:49:35Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2001.09055","created_at":"2026-07-05T01:49:35Z"},{"alias_kind":"pith_short_12","alias_value":"HCXX5C6VZG7L","created_at":"2026-07-05T01:49:35Z"},{"alias_kind":"pith_short_16","alias_value":"HCXX5C6VZG7LVA52","created_at":"2026-07-05T01:49:35Z"},{"alias_kind":"pith_short_8","alias_value":"HCXX5C6V","created_at":"2026-07-05T01:49:35Z"}],"graph_snapshots":[{"event_id":"sha256:f967caea50ebcdae17015b9949cedef31df51d8e8be3c7260ea85b36a67d209b","target":"graph","created_at":"2026-07-05T01:49:35Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2001.09055/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The emerge of new technologies to synthesize and analyze big data with high-performance computing, has increased our capacity to more accurately predict crop yields. Recent research has shown that Machine learning (ML) can provide reasonable predictions, faster, and with higher flexibility compared to simulation crop modeling. The earlier the prediction during the growing season the better, but this has not been thoroughly investigated as previous studies considered all data available to predict yields. This paper provides a machine learning based framework to forecast corn yields in three US ","authors_text":"Guiping Hu, Mohsen Shahhosseini, Sotirios V. Archontoulis","cross_cats":["cs.LG","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2020-01-18T03:55:20Z","title":"Forecasting Corn Yield with Machine Learning Ensembles"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2001.09055","kind":"arxiv","version":2},"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":"sha256:57a41284f26337761403c28d2d3ba5f04198bcd8b55972becf22edc3ca8220d7","target":"record","created_at":"2026-07-05T01:49:35Z","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":"6da11dd07fc02969c7f0f5e1651201794f6cc6c43957e305dfe11f40fe31e1c5","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2020-01-18T03:55:20Z","title_canon_sha256":"5e4991ae0f0fda2d213dffae112a2fbe5dc4e33a17988c2a2c8e5e64fb9eb351"},"schema_version":"1.0","source":{"id":"2001.09055","kind":"arxiv","version":2}},"canonical_sha256":"38af7e8bd5c9beba83bae4639f5740f23d274d069d3a8a36c21454774fc7c5c4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"38af7e8bd5c9beba83bae4639f5740f23d274d069d3a8a36c21454774fc7c5c4","first_computed_at":"2026-07-05T01:49:35.790627Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:49:35.790627Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"hZijq77pV45/1uWaunUAjfLGI5IgY/++BbSw2XlljiK6fZkTtoBY5/n464hY1g6PUhNGjHH6l0MpUOTCRGbjAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T01:49:35.791184Z","signed_message":"canonical_sha256_bytes"},"source_id":"2001.09055","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:57a41284f26337761403c28d2d3ba5f04198bcd8b55972becf22edc3ca8220d7","sha256:f967caea50ebcdae17015b9949cedef31df51d8e8be3c7260ea85b36a67d209b"],"state_sha256":"3dcc67af7c1c665868d5e359db8944c7fb30af340315beba3d651289ae93b8a3"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xvsLyKgrXv2jv4SKM3tDgOEQG+taGf2mzzAeYS8FJz4U0l/Pam5C72nwa6VQx5lsI+3GzcfbtxE3RfVUY+tKCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-23T00:36:16.938131Z","bundle_sha256":"0897e483c900bf44968e3faf8c90c55a63b610ca5d896128d4ba59c8a2f2409c"}}