{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:7HXD4VXSPDPIQ7ARRKXVS5XHLJ","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":"a080b23eeb3d24c57849a611ca388c55330fee319ad063b81607ba8323984a56","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-19T14:23:51Z","title_canon_sha256":"ad992410528028f8b4da4c1a3317cbec9ccec43a37912e65198eb2fe1be4feef"},"schema_version":"1.0","source":{"id":"2606.21475","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.21475","created_at":"2026-06-23T01:13:11Z"},{"alias_kind":"arxiv_version","alias_value":"2606.21475v1","created_at":"2026-06-23T01:13:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.21475","created_at":"2026-06-23T01:13:11Z"},{"alias_kind":"pith_short_12","alias_value":"7HXD4VXSPDPI","created_at":"2026-06-23T01:13:11Z"},{"alias_kind":"pith_short_16","alias_value":"7HXD4VXSPDPIQ7AR","created_at":"2026-06-23T01:13:11Z"},{"alias_kind":"pith_short_8","alias_value":"7HXD4VXS","created_at":"2026-06-23T01:13:11Z"}],"graph_snapshots":[{"event_id":"sha256:1ecfcc75fee2cfb6f832bc41f471b24150122c8f73373ef3ffc4bf3e6e61a32f","target":"graph","created_at":"2026-06-23T01:13:11Z","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/2606.21475/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Accurate soil moisture estimation in semi-arid agricultural regions requires integrating remote sensing and meteorological information while accounting for the delayed response of soil moisture to atmospheric forcing. This study introduces a Cross-Correlation Function (CCF) methodology to determine optimal temporal lags (0-30 days) between meteorological variables and soil moisture, as well as inter-depth lags (0-15 days) describing vertical moisture propagation from the surface (10 cm) to deeper layers (20-50 cm). The approach was validated across seven agricultural plots in southeastern Spai","authors_text":"Adrian Canovas-Rodriguez, Antonio F. Skarmeta, Aurora Gonz\\'alez Vidal","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-19T14:23:51Z","title":"Deep Learning for Soil Moisture Estimation: Fusing Satellite Data with Optimally-Lagged Meteorological Features"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.21475","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:e5380313bd0194bb68084fd0e7e4c881fa251fff688827dc266945cae66e3587","target":"record","created_at":"2026-06-23T01:13:11Z","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":"a080b23eeb3d24c57849a611ca388c55330fee319ad063b81607ba8323984a56","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-19T14:23:51Z","title_canon_sha256":"ad992410528028f8b4da4c1a3317cbec9ccec43a37912e65198eb2fe1be4feef"},"schema_version":"1.0","source":{"id":"2606.21475","kind":"arxiv","version":1}},"canonical_sha256":"f9ee3e56f278de887c118aaf5976e75a5286ca6d9e572a55f0704b70c5a76986","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f9ee3e56f278de887c118aaf5976e75a5286ca6d9e572a55f0704b70c5a76986","first_computed_at":"2026-06-23T01:13:11.615048Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-23T01:13:11.615048Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"2EI+IOwqZm4D75goQ5nIKKLdppQfts6mZuxICpPrwK8byxskydKCvuL9y+0vKXCEUP/+PomJSpyMQx3sbOAXAw==","signature_status":"signed_v1","signed_at":"2026-06-23T01:13:11.615505Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.21475","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e5380313bd0194bb68084fd0e7e4c881fa251fff688827dc266945cae66e3587","sha256:1ecfcc75fee2cfb6f832bc41f471b24150122c8f73373ef3ffc4bf3e6e61a32f"],"state_sha256":"e1f3f7530e4a1487deb43ab60a88e045c0c7ff13192ae4e2946640284deedb12"}