{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:6EBYWVMLEALWBIVU3AJQBXHGR7","short_pith_number":"pith:6EBYWVML","schema_version":"1.0","canonical_sha256":"f1038b558b201760a2b4d81300dce68fe3ddbd5a0391d2bb9f8261037e85b0f9","source":{"kind":"arxiv","id":"1906.08843","version":1},"attestation_state":"computed","paper":{"title":"On Statistical Properties of A Veracity Scoring Method for Spatial Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.ST","stat.AP","stat.TH"],"primary_cat":"stat.ME","authors_text":"Arnab Chakraborty, Soumendra N. Lahiri","submitted_at":"2019-06-20T20:47:38Z","abstract_excerpt":"Measuring veracity or reliability of noisy data is of utmost importance, especially in the scenarios where the information are gathered through automated systems. In a recent paper, Chakraborty et. al. (2019) have introduced a veracity scoring technique for geostatistical data. The authors have used a high-quality `reference' data to measure the veracity of the varying-quality observations and incorporated the veracity scores in their analysis of mobile-sensor generated noisy weather data to generate efficient predictions of the ambient temperature process. In this paper, we consider the scena"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1906.08843","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2019-06-20T20:47:38Z","cross_cats_sorted":["math.ST","stat.AP","stat.TH"],"title_canon_sha256":"8256f1a636fee9704d6c0593fe658050ca42e3dee533d4843515974282be30ab","abstract_canon_sha256":"44bcbef8be4f45fb66b971a5ec04b54cfac9c79908862bcaaa4470f1071698ce"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:42:47.754905Z","signature_b64":"rptn5w+wiBx8S0ZobpLE8o171BG1vpmzU/7Mv0Mj21W8qw9rFZcddvdughWtgReRqKR/zQJD6TuuzUR/CMxWBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f1038b558b201760a2b4d81300dce68fe3ddbd5a0391d2bb9f8261037e85b0f9","last_reissued_at":"2026-05-17T23:42:47.754247Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:42:47.754247Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"On Statistical Properties of A Veracity Scoring Method for Spatial Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.ST","stat.AP","stat.TH"],"primary_cat":"stat.ME","authors_text":"Arnab Chakraborty, Soumendra N. Lahiri","submitted_at":"2019-06-20T20:47:38Z","abstract_excerpt":"Measuring veracity or reliability of noisy data is of utmost importance, especially in the scenarios where the information are gathered through automated systems. In a recent paper, Chakraborty et. al. (2019) have introduced a veracity scoring technique for geostatistical data. The authors have used a high-quality `reference' data to measure the veracity of the varying-quality observations and incorporated the veracity scores in their analysis of mobile-sensor generated noisy weather data to generate efficient predictions of the ambient temperature process. In this paper, we consider the scena"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.08843","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":""},"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1906.08843","created_at":"2026-05-17T23:42:47.754363+00:00"},{"alias_kind":"arxiv_version","alias_value":"1906.08843v1","created_at":"2026-05-17T23:42:47.754363+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.08843","created_at":"2026-05-17T23:42:47.754363+00:00"},{"alias_kind":"pith_short_12","alias_value":"6EBYWVMLEALW","created_at":"2026-05-18T12:33:10.108867+00:00"},{"alias_kind":"pith_short_16","alias_value":"6EBYWVMLEALWBIVU","created_at":"2026-05-18T12:33:10.108867+00:00"},{"alias_kind":"pith_short_8","alias_value":"6EBYWVML","created_at":"2026-05-18T12:33:10.108867+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/6EBYWVMLEALWBIVU3AJQBXHGR7","json":"https://pith.science/pith/6EBYWVMLEALWBIVU3AJQBXHGR7.json","graph_json":"https://pith.science/api/pith-number/6EBYWVMLEALWBIVU3AJQBXHGR7/graph.json","events_json":"https://pith.science/api/pith-number/6EBYWVMLEALWBIVU3AJQBXHGR7/events.json","paper":"https://pith.science/paper/6EBYWVML"},"agent_actions":{"view_html":"https://pith.science/pith/6EBYWVMLEALWBIVU3AJQBXHGR7","download_json":"https://pith.science/pith/6EBYWVMLEALWBIVU3AJQBXHGR7.json","view_paper":"https://pith.science/paper/6EBYWVML","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1906.08843&json=true","fetch_graph":"https://pith.science/api/pith-number/6EBYWVMLEALWBIVU3AJQBXHGR7/graph.json","fetch_events":"https://pith.science/api/pith-number/6EBYWVMLEALWBIVU3AJQBXHGR7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/6EBYWVMLEALWBIVU3AJQBXHGR7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/6EBYWVMLEALWBIVU3AJQBXHGR7/action/storage_attestation","attest_author":"https://pith.science/pith/6EBYWVMLEALWBIVU3AJQBXHGR7/action/author_attestation","sign_citation":"https://pith.science/pith/6EBYWVMLEALWBIVU3AJQBXHGR7/action/citation_signature","submit_replication":"https://pith.science/pith/6EBYWVMLEALWBIVU3AJQBXHGR7/action/replication_record"}},"created_at":"2026-05-17T23:42:47.754363+00:00","updated_at":"2026-05-17T23:42:47.754363+00:00"}