{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:2BWHRVYRGDPVO6VMFGNXIXXUV6","short_pith_number":"pith:2BWHRVYR","canonical_record":{"source":{"id":"2406.07094","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2024-06-11T09:33:15Z","cross_cats_sorted":[],"title_canon_sha256":"473f1529e12f3b13d01533f4f50807a308c616d0155e4ac5504866ef1cfd9a5d","abstract_canon_sha256":"8f14aff1a1774e2c363438eca538777a757de188c14a7e9ba6d02334e6c5ad10"},"schema_version":"1.0"},"canonical_sha256":"d06c78d71130df577aac299b745ef4af99df20e7b69afda6b4eff73fe943c223","source":{"kind":"arxiv","id":"2406.07094","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2406.07094","created_at":"2026-07-05T08:30:18Z"},{"alias_kind":"arxiv_version","alias_value":"2406.07094v1","created_at":"2026-07-05T08:30:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.07094","created_at":"2026-07-05T08:30:18Z"},{"alias_kind":"pith_short_12","alias_value":"2BWHRVYRGDPV","created_at":"2026-07-05T08:30:18Z"},{"alias_kind":"pith_short_16","alias_value":"2BWHRVYRGDPVO6VM","created_at":"2026-07-05T08:30:18Z"},{"alias_kind":"pith_short_8","alias_value":"2BWHRVYR","created_at":"2026-07-05T08:30:18Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:2BWHRVYRGDPVO6VMFGNXIXXUV6","target":"record","payload":{"canonical_record":{"source":{"id":"2406.07094","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2024-06-11T09:33:15Z","cross_cats_sorted":[],"title_canon_sha256":"473f1529e12f3b13d01533f4f50807a308c616d0155e4ac5504866ef1cfd9a5d","abstract_canon_sha256":"8f14aff1a1774e2c363438eca538777a757de188c14a7e9ba6d02334e6c5ad10"},"schema_version":"1.0"},"canonical_sha256":"d06c78d71130df577aac299b745ef4af99df20e7b69afda6b4eff73fe943c223","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:30:18.334689Z","signature_b64":"M8rCi7XJ2Qym89OzR0HH1KSRAWhWdhVLMR+JcbVGARur1H+NkRfkj8ng15TThnq++oI60oeVR17w/yC+8XxNAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d06c78d71130df577aac299b745ef4af99df20e7b69afda6b4eff73fe943c223","last_reissued_at":"2026-07-05T08:30:18.334171Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:30:18.334171Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2406.07094","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":"2026-07-05T08:30:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+S3h2PVCAH+buYu7FB5aswXMCavvId+YfZr7+1Nb0BRfRvLVhDi0c11DCycJO8NPwTZVi/vU7vlrE9nwGOwsCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T16:58:27.530885Z"},"content_sha256":"58fe818ec968c164aaa58ffbc2ed0f2a7ac496f7ee94f6e1598448bc3f6dbc6d","schema_version":"1.0","event_id":"sha256:58fe818ec968c164aaa58ffbc2ed0f2a7ac496f7ee94f6e1598448bc3f6dbc6d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:2BWHRVYRGDPVO6VMFGNXIXXUV6","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Grapevine Disease Prediction Using Climate Variables from Multi-Sensor Remote Sensing Imagery via a Transformer Model","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.IR","authors_text":"Natalia Efremova, Weiying Zhao","submitted_at":"2024-06-11T09:33:15Z","abstract_excerpt":"Early detection and management of grapevine diseases are important in pursuing sustainable viticulture. This paper introduces a novel framework leveraging the TabPFN model to forecast blockwise grapevine diseases using climate variables from multi-sensor remote sensing imagery. By integrating advanced machine learning techniques with detailed environmental data, our approach significantly enhances the accuracy and efficiency of disease prediction in vineyards. The TabPFN model's experimental evaluations showcase comparable performance to traditional gradient-boosted decision trees, such as XGB"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.07094","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/2406.07094/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":"2026-07-05T08:30:18Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zXccNAATf8+JMI6H8bmop6TmjpBsbW05JdGtodJWNn3tfPubg6RXWjTOQZjvxwePYVOZZxAzkFz9MNo4+n53Cw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T16:58:27.531722Z"},"content_sha256":"abffd8f4b0dc67a9555fff511d5ec1c6c4b7aa100785d1ce3838c1428b44119b","schema_version":"1.0","event_id":"sha256:abffd8f4b0dc67a9555fff511d5ec1c6c4b7aa100785d1ce3838c1428b44119b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/2BWHRVYRGDPVO6VMFGNXIXXUV6/bundle.json","state_url":"https://pith.science/pith/2BWHRVYRGDPVO6VMFGNXIXXUV6/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/2BWHRVYRGDPVO6VMFGNXIXXUV6/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":"2026-08-16T16:58:27Z","links":{"resolver":"https://pith.science/pith/2BWHRVYRGDPVO6VMFGNXIXXUV6","bundle":"https://pith.science/pith/2BWHRVYRGDPVO6VMFGNXIXXUV6/bundle.json","state":"https://pith.science/pith/2BWHRVYRGDPVO6VMFGNXIXXUV6/state.json","well_known_bundle":"https://pith.science/.well-known/pith/2BWHRVYRGDPVO6VMFGNXIXXUV6/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:2BWHRVYRGDPVO6VMFGNXIXXUV6","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":"8f14aff1a1774e2c363438eca538777a757de188c14a7e9ba6d02334e6c5ad10","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2024-06-11T09:33:15Z","title_canon_sha256":"473f1529e12f3b13d01533f4f50807a308c616d0155e4ac5504866ef1cfd9a5d"},"schema_version":"1.0","source":{"id":"2406.07094","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2406.07094","created_at":"2026-07-05T08:30:18Z"},{"alias_kind":"arxiv_version","alias_value":"2406.07094v1","created_at":"2026-07-05T08:30:18Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.07094","created_at":"2026-07-05T08:30:18Z"},{"alias_kind":"pith_short_12","alias_value":"2BWHRVYRGDPV","created_at":"2026-07-05T08:30:18Z"},{"alias_kind":"pith_short_16","alias_value":"2BWHRVYRGDPVO6VM","created_at":"2026-07-05T08:30:18Z"},{"alias_kind":"pith_short_8","alias_value":"2BWHRVYR","created_at":"2026-07-05T08:30:18Z"}],"graph_snapshots":[{"event_id":"sha256:abffd8f4b0dc67a9555fff511d5ec1c6c4b7aa100785d1ce3838c1428b44119b","target":"graph","created_at":"2026-07-05T08:30:18Z","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/2406.07094/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Early detection and management of grapevine diseases are important in pursuing sustainable viticulture. This paper introduces a novel framework leveraging the TabPFN model to forecast blockwise grapevine diseases using climate variables from multi-sensor remote sensing imagery. By integrating advanced machine learning techniques with detailed environmental data, our approach significantly enhances the accuracy and efficiency of disease prediction in vineyards. The TabPFN model's experimental evaluations showcase comparable performance to traditional gradient-boosted decision trees, such as XGB","authors_text":"Natalia Efremova, Weiying Zhao","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2024-06-11T09:33:15Z","title":"Grapevine Disease Prediction Using Climate Variables from Multi-Sensor Remote Sensing Imagery via a Transformer Model"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.07094","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":"sha256:58fe818ec968c164aaa58ffbc2ed0f2a7ac496f7ee94f6e1598448bc3f6dbc6d","target":"record","created_at":"2026-07-05T08:30:18Z","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":"8f14aff1a1774e2c363438eca538777a757de188c14a7e9ba6d02334e6c5ad10","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.IR","submitted_at":"2024-06-11T09:33:15Z","title_canon_sha256":"473f1529e12f3b13d01533f4f50807a308c616d0155e4ac5504866ef1cfd9a5d"},"schema_version":"1.0","source":{"id":"2406.07094","kind":"arxiv","version":1}},"canonical_sha256":"d06c78d71130df577aac299b745ef4af99df20e7b69afda6b4eff73fe943c223","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d06c78d71130df577aac299b745ef4af99df20e7b69afda6b4eff73fe943c223","first_computed_at":"2026-07-05T08:30:18.334171Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:30:18.334171Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"M8rCi7XJ2Qym89OzR0HH1KSRAWhWdhVLMR+JcbVGARur1H+NkRfkj8ng15TThnq++oI60oeVR17w/yC+8XxNAg==","signature_status":"signed_v1","signed_at":"2026-07-05T08:30:18.334689Z","signed_message":"canonical_sha256_bytes"},"source_id":"2406.07094","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:58fe818ec968c164aaa58ffbc2ed0f2a7ac496f7ee94f6e1598448bc3f6dbc6d","sha256:abffd8f4b0dc67a9555fff511d5ec1c6c4b7aa100785d1ce3838c1428b44119b"],"state_sha256":"fc04f7cbd1d85e31e58adbe9b180dd50e762bb4c981a973c0f6dca573f6bc480"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"b1+RaAVnwyeR6mwwJmmz0zwsk2gyQxrk0CIiDyB00geLoYWzaE1K5cFCMK8okNKGNtY3KD0jyMyfmOYm/NsJAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-16T16:58:27.538680Z","bundle_sha256":"69ae60108a3f1964bc1f5218ed1e0b274aa4bc99cde32232e33e94ecb808f8a2"}}