{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:46N74TA4OT6XNMBDLO5HRGFGNB","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":"2b5f0b5698d6bbe78f0f54cb864ae91c6932e9c7b058c7d8594ef9cd1868a8e5","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-02-13T23:20:22Z","title_canon_sha256":"442dca5b07adc226c88bbc15c56ee143468b9c9d13ed3dd066d995558c754863"},"schema_version":"1.0","source":{"id":"1702.04018","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1702.04018","created_at":"2026-05-18T00:50:49Z"},{"alias_kind":"arxiv_version","alias_value":"1702.04018v1","created_at":"2026-05-18T00:50:49Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1702.04018","created_at":"2026-05-18T00:50:49Z"},{"alias_kind":"pith_short_12","alias_value":"46N74TA4OT6X","created_at":"2026-05-18T12:30:58Z"},{"alias_kind":"pith_short_16","alias_value":"46N74TA4OT6XNMBD","created_at":"2026-05-18T12:30:58Z"},{"alias_kind":"pith_short_8","alias_value":"46N74TA4","created_at":"2026-05-18T12:30:58Z"}],"graph_snapshots":[{"event_id":"sha256:4c64474302eaf37fed815d6fa3eb31e54a73f0076828235f8de138348cfa11ea","target":"graph","created_at":"2026-05-18T00:50:49Z","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"},"paper":{"abstract_excerpt":"Statistical downscaling of global climate models (GCMs) allows researchers to study local climate change effects decades into the future. A wide range of statistical models have been applied to downscaling GCMs but recent advances in machine learning have not been explored. In this paper, we compare four fundamental statistical methods, Bias Correction Spatial Disaggregation (BCSD), Ordinary Least Squares, Elastic-Net, and Support Vector Machine, with three more advanced machine learning methods, Multi-task Sparse Structure Learning (MSSL), BCSD coupled with MSSL, and Convolutional Neural Netw","authors_text":"Auroop R Ganguly, Evan Kodra, Thomas Vandal","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-02-13T23:20:22Z","title":"Intercomparison of Machine Learning Methods for Statistical Downscaling: The Case of Daily and Extreme Precipitation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1702.04018","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":"sha256:34784691ca620f88453cda01975a984f5797812749aa80a6078a1205dc28f243","target":"record","created_at":"2026-05-18T00:50:49Z","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":"2b5f0b5698d6bbe78f0f54cb864ae91c6932e9c7b058c7d8594ef9cd1868a8e5","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-02-13T23:20:22Z","title_canon_sha256":"442dca5b07adc226c88bbc15c56ee143468b9c9d13ed3dd066d995558c754863"},"schema_version":"1.0","source":{"id":"1702.04018","kind":"arxiv","version":1}},"canonical_sha256":"e79bfe4c1c74fd76b0235bba7898a6687af4c2e84b012ea61b877055e1552555","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e79bfe4c1c74fd76b0235bba7898a6687af4c2e84b012ea61b877055e1552555","first_computed_at":"2026-05-18T00:50:49.248280Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:50:49.248280Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"lARiMYyxePKINbfbYdC4eqsGR41fVzqodTYW22TCFBXtjN9/WpnRjarEw9CbxN0u3iv0QnCKydDa4HCIgIXADw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:50:49.248815Z","signed_message":"canonical_sha256_bytes"},"source_id":"1702.04018","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:34784691ca620f88453cda01975a984f5797812749aa80a6078a1205dc28f243","sha256:4c64474302eaf37fed815d6fa3eb31e54a73f0076828235f8de138348cfa11ea"],"state_sha256":"f57f4f3da964132ccb414a48538f36abf6e0c35554b8bbc87a3ba72f3cd26e9a"}