{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:OOB3B6FRBCGTUA6AZS35IVCFUO","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":"13f6656249cabb9d496ebbe4f5e0fe7466668d9ea3c48d44c74b6af1ff7d5fc7","cross_cats_sorted":["cs.CE"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-06-05T23:16:38Z","title_canon_sha256":"9c9e55d5a7b01ee38d23743e9ebcaacee896b08ae940a6981f56ee8ab80dc5c7"},"schema_version":"1.0","source":{"id":"2606.07898","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.07898","created_at":"2026-06-09T01:04:54Z"},{"alias_kind":"arxiv_version","alias_value":"2606.07898v1","created_at":"2026-06-09T01:04:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.07898","created_at":"2026-06-09T01:04:54Z"},{"alias_kind":"pith_short_12","alias_value":"OOB3B6FRBCGT","created_at":"2026-06-09T01:04:54Z"},{"alias_kind":"pith_short_16","alias_value":"OOB3B6FRBCGTUA6A","created_at":"2026-06-09T01:04:54Z"},{"alias_kind":"pith_short_8","alias_value":"OOB3B6FR","created_at":"2026-06-09T01:04:54Z"}],"graph_snapshots":[{"event_id":"sha256:ab95db4fff4a8a62ab2f4df7dbb2df00ecbef7ce0e21eabb624189b8b3d1f7c5","target":"graph","created_at":"2026-06-09T01:04:54Z","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/2606.07898/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"High-resolution regional climate simulations provide critical information for climate impacts assessments but remain computationally expensive, motivating the development of machine-learning downscalers and emulators. A key challenge is determining how limited high-resolution simulations should be distributed across a changing climate trajectory to capture both forced climate response and internal variability. Using the CESM2 Large Ensemble over the western United States, we compare three training-year selection strategies under fixed data budgets: a contiguous block of historical years, years","authors_text":"Alex Hall, Chad W. Thackeray, Karandeep Singh, Stefan Rahimi, Stephen Cropper","cross_cats":["cs.CE"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-06-05T23:16:38Z","title":"Temporal Coverage over Density: Parsimonious Training-Set Design for ML Climate Downscaling"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.07898","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":"sha256:75c10399402b698248c5e968cb41e4c33d8ae1ed76c55b9da3062907a90e9574","target":"record","created_at":"2026-06-09T01:04:54Z","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":"13f6656249cabb9d496ebbe4f5e0fe7466668d9ea3c48d44c74b6af1ff7d5fc7","cross_cats_sorted":["cs.CE"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2026-06-05T23:16:38Z","title_canon_sha256":"9c9e55d5a7b01ee38d23743e9ebcaacee896b08ae940a6981f56ee8ab80dc5c7"},"schema_version":"1.0","source":{"id":"2606.07898","kind":"arxiv","version":1}},"canonical_sha256":"7383b0f8b1088d3a03c0ccb7d45445a3978714c7284908ec93ef546d10873d32","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7383b0f8b1088d3a03c0ccb7d45445a3978714c7284908ec93ef546d10873d32","first_computed_at":"2026-06-09T01:04:54.830115Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-09T01:04:54.830115Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"fpp70hyzeMNiZy7Ysgb7OWLzCtOZtr79THd3gj2j+/z03f4Bki/1wXlInn2uaCr2mTpis9DCcw/MqPyzMH/vDw==","signature_status":"signed_v1","signed_at":"2026-06-09T01:04:54.830546Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.07898","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:75c10399402b698248c5e968cb41e4c33d8ae1ed76c55b9da3062907a90e9574","sha256:ab95db4fff4a8a62ab2f4df7dbb2df00ecbef7ce0e21eabb624189b8b3d1f7c5"],"state_sha256":"bb9c7840cc5e778f3a9326667606d3109397deeb9ba2dda534e99029a706efa1"}