{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:D5BPM6UPRIG7SMVLEOTLPOVUYU","short_pith_number":"pith:D5BPM6UP","canonical_record":{"source":{"id":"2605.30167","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"stat.ML","submitted_at":"2026-05-28T16:19:21Z","cross_cats_sorted":["cs.CV","cs.LG","stat.AP"],"title_canon_sha256":"35d33636d6d3bbc9b7824d7cc3dcfc0643a0c5daf74a70059e0ceab4edb98aa6","abstract_canon_sha256":"f1c23e896348ca5743b6a1e7d09cac1e60e0a22168f72683a9f475ab79695a2e"},"schema_version":"1.0"},"canonical_sha256":"1f42f67a8f8a0df932ab23a6b7bab4c52c31bc2c7ceb1829539fffa0f763cc6e","source":{"kind":"arxiv","id":"2605.30167","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.30167","created_at":"2026-05-29T02:06:11Z"},{"alias_kind":"arxiv_version","alias_value":"2605.30167v1","created_at":"2026-05-29T02:06:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.30167","created_at":"2026-05-29T02:06:11Z"},{"alias_kind":"pith_short_12","alias_value":"D5BPM6UPRIG7","created_at":"2026-05-29T02:06:11Z"},{"alias_kind":"pith_short_16","alias_value":"D5BPM6UPRIG7SMVL","created_at":"2026-05-29T02:06:11Z"},{"alias_kind":"pith_short_8","alias_value":"D5BPM6UP","created_at":"2026-05-29T02:06:11Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:D5BPM6UPRIG7SMVLEOTLPOVUYU","target":"record","payload":{"canonical_record":{"source":{"id":"2605.30167","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"stat.ML","submitted_at":"2026-05-28T16:19:21Z","cross_cats_sorted":["cs.CV","cs.LG","stat.AP"],"title_canon_sha256":"35d33636d6d3bbc9b7824d7cc3dcfc0643a0c5daf74a70059e0ceab4edb98aa6","abstract_canon_sha256":"f1c23e896348ca5743b6a1e7d09cac1e60e0a22168f72683a9f475ab79695a2e"},"schema_version":"1.0"},"canonical_sha256":"1f42f67a8f8a0df932ab23a6b7bab4c52c31bc2c7ceb1829539fffa0f763cc6e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-29T02:06:11.572781Z","signature_b64":"u+jr9NtfKp4J0JLcCfKCY1xNt0ZZxd2bSD1LYbAAZuSH8jbQwb+TjyLCY6kQT50Nd7bAIkUj7w8a+RVTnsk5AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1f42f67a8f8a0df932ab23a6b7bab4c52c31bc2c7ceb1829539fffa0f763cc6e","last_reissued_at":"2026-05-29T02:06:11.572263Z","signature_status":"signed_v1","first_computed_at":"2026-05-29T02:06:11.572263Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.30167","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":"2026-05-29T02:06:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1tiiSEC7Joitjt1t5nIeDVmWBgqfbhVMM1e8LDVhSpja5oX7zNzyyxlzZyBoDpCWeByPEVdUm6UDYtAAdKUmAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-11T14:09:02.381879Z"},"content_sha256":"86eaec049e744b27090ac0a3b77099601d15f84ed8bf8f4d9af3db7df1144bb3","schema_version":"1.0","event_id":"sha256:86eaec049e744b27090ac0a3b77099601d15f84ed8bf8f4d9af3db7df1144bb3"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:D5BPM6UPRIG7SMVLEOTLPOVUYU","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Visual Spatial Learning: Single-Field Spatial Interpolation Using Convolutional Neural Networks","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.CV","cs.LG","stat.AP"],"primary_cat":"stat.ML","authors_text":"Alexandra Silva, Carlos Baquero, Daniel Tinoco, Raquel Menezes","submitted_at":"2026-05-28T16:19:21Z","abstract_excerpt":"Predicting a complete spatially correlated field from sparse observations is a fundamental challenge in spatial statistics and environmental modelling. Classical interpolation methods such as Kriging rely on Gaussian process assumptions and variography, which can limit their effectiveness in non-stationary settings and require substantial domain expertise. In this work, we leverage an architecture based on convolutional neural networks (CNNs) for spatial interpolation that is trained and applied on a single partially observed field, without access to external data or prior fields. The model is"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.30167","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.30167/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":"2026-05-29T02:06:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"amOGT/Y/rg3MspEMwU4Slge3BSAs3rJdfqhIS6dMhjRZi15uNr+rssbwHpcTVEr63jYcKqKBtr8XwuzEe9PaDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-11T14:09:02.382272Z"},"content_sha256":"6f8dfc10756b5ea14cb02f3dbd0d8d9afe05b441cc1a8357cd0af1a1cc865cd5","schema_version":"1.0","event_id":"sha256:6f8dfc10756b5ea14cb02f3dbd0d8d9afe05b441cc1a8357cd0af1a1cc865cd5"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/D5BPM6UPRIG7SMVLEOTLPOVUYU/bundle.json","state_url":"https://pith.science/pith/D5BPM6UPRIG7SMVLEOTLPOVUYU/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/D5BPM6UPRIG7SMVLEOTLPOVUYU/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":"2026-06-11T14:09:02Z","links":{"resolver":"https://pith.science/pith/D5BPM6UPRIG7SMVLEOTLPOVUYU","bundle":"https://pith.science/pith/D5BPM6UPRIG7SMVLEOTLPOVUYU/bundle.json","state":"https://pith.science/pith/D5BPM6UPRIG7SMVLEOTLPOVUYU/state.json","well_known_bundle":"https://pith.science/.well-known/pith/D5BPM6UPRIG7SMVLEOTLPOVUYU/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:D5BPM6UPRIG7SMVLEOTLPOVUYU","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":"f1c23e896348ca5743b6a1e7d09cac1e60e0a22168f72683a9f475ab79695a2e","cross_cats_sorted":["cs.CV","cs.LG","stat.AP"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"stat.ML","submitted_at":"2026-05-28T16:19:21Z","title_canon_sha256":"35d33636d6d3bbc9b7824d7cc3dcfc0643a0c5daf74a70059e0ceab4edb98aa6"},"schema_version":"1.0","source":{"id":"2605.30167","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.30167","created_at":"2026-05-29T02:06:11Z"},{"alias_kind":"arxiv_version","alias_value":"2605.30167v1","created_at":"2026-05-29T02:06:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.30167","created_at":"2026-05-29T02:06:11Z"},{"alias_kind":"pith_short_12","alias_value":"D5BPM6UPRIG7","created_at":"2026-05-29T02:06:11Z"},{"alias_kind":"pith_short_16","alias_value":"D5BPM6UPRIG7SMVL","created_at":"2026-05-29T02:06:11Z"},{"alias_kind":"pith_short_8","alias_value":"D5BPM6UP","created_at":"2026-05-29T02:06:11Z"}],"graph_snapshots":[{"event_id":"sha256:6f8dfc10756b5ea14cb02f3dbd0d8d9afe05b441cc1a8357cd0af1a1cc865cd5","target":"graph","created_at":"2026-05-29T02:06:11Z","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/2605.30167/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Predicting a complete spatially correlated field from sparse observations is a fundamental challenge in spatial statistics and environmental modelling. Classical interpolation methods such as Kriging rely on Gaussian process assumptions and variography, which can limit their effectiveness in non-stationary settings and require substantial domain expertise. In this work, we leverage an architecture based on convolutional neural networks (CNNs) for spatial interpolation that is trained and applied on a single partially observed field, without access to external data or prior fields. The model is","authors_text":"Alexandra Silva, Carlos Baquero, Daniel Tinoco, Raquel Menezes","cross_cats":["cs.CV","cs.LG","stat.AP"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"stat.ML","submitted_at":"2026-05-28T16:19:21Z","title":"Visual Spatial Learning: Single-Field Spatial Interpolation Using Convolutional Neural Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.30167","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":"sha256:86eaec049e744b27090ac0a3b77099601d15f84ed8bf8f4d9af3db7df1144bb3","target":"record","created_at":"2026-05-29T02:06:11Z","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":"f1c23e896348ca5743b6a1e7d09cac1e60e0a22168f72683a9f475ab79695a2e","cross_cats_sorted":["cs.CV","cs.LG","stat.AP"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"stat.ML","submitted_at":"2026-05-28T16:19:21Z","title_canon_sha256":"35d33636d6d3bbc9b7824d7cc3dcfc0643a0c5daf74a70059e0ceab4edb98aa6"},"schema_version":"1.0","source":{"id":"2605.30167","kind":"arxiv","version":1}},"canonical_sha256":"1f42f67a8f8a0df932ab23a6b7bab4c52c31bc2c7ceb1829539fffa0f763cc6e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1f42f67a8f8a0df932ab23a6b7bab4c52c31bc2c7ceb1829539fffa0f763cc6e","first_computed_at":"2026-05-29T02:06:11.572263Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-29T02:06:11.572263Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"u+jr9NtfKp4J0JLcCfKCY1xNt0ZZxd2bSD1LYbAAZuSH8jbQwb+TjyLCY6kQT50Nd7bAIkUj7w8a+RVTnsk5AQ==","signature_status":"signed_v1","signed_at":"2026-05-29T02:06:11.572781Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.30167","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:86eaec049e744b27090ac0a3b77099601d15f84ed8bf8f4d9af3db7df1144bb3","sha256:6f8dfc10756b5ea14cb02f3dbd0d8d9afe05b441cc1a8357cd0af1a1cc865cd5"],"state_sha256":"0abdcac92702c790f57712ec21a87f330f8cbba6c9e0f6f88fb9ac0fc7ce99b8"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"R0+mLRy+X8SPC40787LVlFh1NDS1yVhtECZK19RvrIS7HwqpvSiNEJr2c1+EQeMRxLo4vyiiAAbBnVlNM2cOCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-11T14:09:02.384279Z","bundle_sha256":"a825ab1909131396bb9865588512cb7f54ea71616d3d86270bfea99c0724a1b2"}}