{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:UHLH3K6RXMWNS4PXDL6TU3IDJQ","short_pith_number":"pith:UHLH3K6R","canonical_record":{"source":{"id":"2007.11972","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2020-07-23T12:38:53Z","cross_cats_sorted":["cs.LG","stat.AP","stat.ME"],"title_canon_sha256":"29a40da3bbdc9dc3ca7de4a0f8d39364639fd02af3c5282b4ab3bc46eec56175","abstract_canon_sha256":"23ba3c02085c54aa83eb316a7c4f0c975a6fcb1689899e171922e825996aae80"},"schema_version":"1.0"},"canonical_sha256":"a1d67dabd1bb2cd971f71afd3a6d034c1b490cc8d7d6c03e607efa624cd1544d","source":{"kind":"arxiv","id":"2007.11972","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2007.11972","created_at":"2026-07-05T04:25:39Z"},{"alias_kind":"arxiv_version","alias_value":"2007.11972v4","created_at":"2026-07-05T04:25:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2007.11972","created_at":"2026-07-05T04:25:39Z"},{"alias_kind":"pith_short_12","alias_value":"UHLH3K6RXMWN","created_at":"2026-07-05T04:25:39Z"},{"alias_kind":"pith_short_16","alias_value":"UHLH3K6RXMWNS4PX","created_at":"2026-07-05T04:25:39Z"},{"alias_kind":"pith_short_8","alias_value":"UHLH3K6R","created_at":"2026-07-05T04:25:39Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:UHLH3K6RXMWNS4PXDL6TU3IDJQ","target":"record","payload":{"canonical_record":{"source":{"id":"2007.11972","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2020-07-23T12:38:53Z","cross_cats_sorted":["cs.LG","stat.AP","stat.ME"],"title_canon_sha256":"29a40da3bbdc9dc3ca7de4a0f8d39364639fd02af3c5282b4ab3bc46eec56175","abstract_canon_sha256":"23ba3c02085c54aa83eb316a7c4f0c975a6fcb1689899e171922e825996aae80"},"schema_version":"1.0"},"canonical_sha256":"a1d67dabd1bb2cd971f71afd3a6d034c1b490cc8d7d6c03e607efa624cd1544d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:25:39.690572Z","signature_b64":"MPZCn0Png2Bqwz4ZWG/0uR8kIlPQAvOILgqfnAMdMXZNVz8AOj8oqRM42rYm5bf4Hn2srqRrND1rOlge7GV2Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a1d67dabd1bb2cd971f71afd3a6d034c1b490cc8d7d6c03e607efa624cd1544d","last_reissued_at":"2026-07-05T04:25:39.690155Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:25:39.690155Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2007.11972","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:25:39Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SEbSXsFER9vV1xAlfoir+mFv1U5YOGvTeNs5+zcl78UElLgU8Isk+LYD0n7SQmelDOcjZF+4Pfw/ZUevn9SrCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T02:52:30.027733Z"},"content_sha256":"47b87e32d9ec1dea03581b6fce136d08d716937a048e0a03f48e5132535c3c60","schema_version":"1.0","event_id":"sha256:47b87e32d9ec1dea03581b6fce136d08d716937a048e0a03f48e5132535c3c60"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:UHLH3K6RXMWNS4PXDL6TU3IDJQ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"DeepKriging: Spatially Dependent Deep Neural Networks for Spatial Prediction","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG","stat.AP","stat.ME"],"primary_cat":"stat.ML","authors_text":"Brian J Reich, Wanfang Chen, Ying Sun, Yuxiao Li","submitted_at":"2020-07-23T12:38:53Z","abstract_excerpt":"In spatial statistics, a common objective is to predict values of a spatial process at unobserved locations by exploiting spatial dependence. Kriging provides the best linear unbiased predictor using covariance functions and is often associated with Gaussian processes. However, when considering non-linear prediction for non-Gaussian and categorical data, the Kriging prediction is no longer optimal, and the associated variance is often overly optimistic. Although deep neural networks (DNNs) are widely used for general classification and prediction, they have not been studied thoroughly for data"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2007.11972","kind":"arxiv","version":4},"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/2007.11972/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":"2026-07-05T04:25:39Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"seIT38PNYUL4wJg2URrYo5ddPMcjtKtentWp1OgKBBG4EJmLZfpHMJHvyYl1j/EwBbmTmxoVHIx1XqAoFfhDDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T02:52:30.028126Z"},"content_sha256":"3a6ec8c47255e76ef5d54f3fa543fc9a333564d7d36f5c9b295dac10d4cde8d4","schema_version":"1.0","event_id":"sha256:3a6ec8c47255e76ef5d54f3fa543fc9a333564d7d36f5c9b295dac10d4cde8d4"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/UHLH3K6RXMWNS4PXDL6TU3IDJQ/bundle.json","state_url":"https://pith.science/pith/UHLH3K6RXMWNS4PXDL6TU3IDJQ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/UHLH3K6RXMWNS4PXDL6TU3IDJQ/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":"2026-08-13T02:52:30Z","links":{"resolver":"https://pith.science/pith/UHLH3K6RXMWNS4PXDL6TU3IDJQ","bundle":"https://pith.science/pith/UHLH3K6RXMWNS4PXDL6TU3IDJQ/bundle.json","state":"https://pith.science/pith/UHLH3K6RXMWNS4PXDL6TU3IDJQ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/UHLH3K6RXMWNS4PXDL6TU3IDJQ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:UHLH3K6RXMWNS4PXDL6TU3IDJQ","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":"23ba3c02085c54aa83eb316a7c4f0c975a6fcb1689899e171922e825996aae80","cross_cats_sorted":["cs.LG","stat.AP","stat.ME"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2020-07-23T12:38:53Z","title_canon_sha256":"29a40da3bbdc9dc3ca7de4a0f8d39364639fd02af3c5282b4ab3bc46eec56175"},"schema_version":"1.0","source":{"id":"2007.11972","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2007.11972","created_at":"2026-07-05T04:25:39Z"},{"alias_kind":"arxiv_version","alias_value":"2007.11972v4","created_at":"2026-07-05T04:25:39Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2007.11972","created_at":"2026-07-05T04:25:39Z"},{"alias_kind":"pith_short_12","alias_value":"UHLH3K6RXMWN","created_at":"2026-07-05T04:25:39Z"},{"alias_kind":"pith_short_16","alias_value":"UHLH3K6RXMWNS4PX","created_at":"2026-07-05T04:25:39Z"},{"alias_kind":"pith_short_8","alias_value":"UHLH3K6R","created_at":"2026-07-05T04:25:39Z"}],"graph_snapshots":[{"event_id":"sha256:3a6ec8c47255e76ef5d54f3fa543fc9a333564d7d36f5c9b295dac10d4cde8d4","target":"graph","created_at":"2026-07-05T04:25:39Z","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/2007.11972/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In spatial statistics, a common objective is to predict values of a spatial process at unobserved locations by exploiting spatial dependence. Kriging provides the best linear unbiased predictor using covariance functions and is often associated with Gaussian processes. However, when considering non-linear prediction for non-Gaussian and categorical data, the Kriging prediction is no longer optimal, and the associated variance is often overly optimistic. Although deep neural networks (DNNs) are widely used for general classification and prediction, they have not been studied thoroughly for data","authors_text":"Brian J Reich, Wanfang Chen, Ying Sun, Yuxiao Li","cross_cats":["cs.LG","stat.AP","stat.ME"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2020-07-23T12:38:53Z","title":"DeepKriging: Spatially Dependent Deep Neural Networks for Spatial Prediction"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2007.11972","kind":"arxiv","version":4},"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":"sha256:47b87e32d9ec1dea03581b6fce136d08d716937a048e0a03f48e5132535c3c60","target":"record","created_at":"2026-07-05T04:25:39Z","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":"23ba3c02085c54aa83eb316a7c4f0c975a6fcb1689899e171922e825996aae80","cross_cats_sorted":["cs.LG","stat.AP","stat.ME"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2020-07-23T12:38:53Z","title_canon_sha256":"29a40da3bbdc9dc3ca7de4a0f8d39364639fd02af3c5282b4ab3bc46eec56175"},"schema_version":"1.0","source":{"id":"2007.11972","kind":"arxiv","version":4}},"canonical_sha256":"a1d67dabd1bb2cd971f71afd3a6d034c1b490cc8d7d6c03e607efa624cd1544d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a1d67dabd1bb2cd971f71afd3a6d034c1b490cc8d7d6c03e607efa624cd1544d","first_computed_at":"2026-07-05T04:25:39.690155Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:25:39.690155Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"MPZCn0Png2Bqwz4ZWG/0uR8kIlPQAvOILgqfnAMdMXZNVz8AOj8oqRM42rYm5bf4Hn2srqRrND1rOlge7GV2Bw==","signature_status":"signed_v1","signed_at":"2026-07-05T04:25:39.690572Z","signed_message":"canonical_sha256_bytes"},"source_id":"2007.11972","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:47b87e32d9ec1dea03581b6fce136d08d716937a048e0a03f48e5132535c3c60","sha256:3a6ec8c47255e76ef5d54f3fa543fc9a333564d7d36f5c9b295dac10d4cde8d4"],"state_sha256":"15e43bcad794e1a088c69a6b29baa23914aa888e3fff2fff4897fbf2940307ea"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LNbLKEkW75s6hWU///7nJ9UmAmuNmq4Bt71LGWvTR4Yi1mZ0vbcYUUBOk/BnuyQe5Jblox0LjsBMlZ0hRZJxDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-13T02:52:30.030814Z","bundle_sha256":"84668511db1159ceb75c8b9fa1e607ca2e2319902dbf848be95e1df2ae730a17"}}