{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:MWHSXXUVAKM55NVX5ZL75YUXBE","short_pith_number":"pith:MWHSXXUV","canonical_record":{"source":{"id":"2607.05655","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.HC","submitted_at":"2026-07-06T21:51:46Z","cross_cats_sorted":[],"title_canon_sha256":"0af84674c51e8a328acc1ad47c7067199d55f2687e6c82b653cd069bc6f729f5","abstract_canon_sha256":"b44cd2c1ef7e9870d6ee60fef6b1f32f685d5687a1a716d4a8efb6275d3efe10"},"schema_version":"1.0"},"canonical_sha256":"658f2bde950299deb6b7ee57fee297091237c0cee1249650629f3af873eb93f5","source":{"kind":"arxiv","id":"2607.05655","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.05655","created_at":"2026-07-08T01:18:40Z"},{"alias_kind":"arxiv_version","alias_value":"2607.05655v1","created_at":"2026-07-08T01:18:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.05655","created_at":"2026-07-08T01:18:40Z"},{"alias_kind":"pith_short_12","alias_value":"MWHSXXUVAKM5","created_at":"2026-07-08T01:18:40Z"},{"alias_kind":"pith_short_16","alias_value":"MWHSXXUVAKM55NVX","created_at":"2026-07-08T01:18:40Z"},{"alias_kind":"pith_short_8","alias_value":"MWHSXXUV","created_at":"2026-07-08T01:18:40Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:MWHSXXUVAKM55NVX5ZL75YUXBE","target":"record","payload":{"canonical_record":{"source":{"id":"2607.05655","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.HC","submitted_at":"2026-07-06T21:51:46Z","cross_cats_sorted":[],"title_canon_sha256":"0af84674c51e8a328acc1ad47c7067199d55f2687e6c82b653cd069bc6f729f5","abstract_canon_sha256":"b44cd2c1ef7e9870d6ee60fef6b1f32f685d5687a1a716d4a8efb6275d3efe10"},"schema_version":"1.0"},"canonical_sha256":"658f2bde950299deb6b7ee57fee297091237c0cee1249650629f3af873eb93f5","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-08T01:18:40.649743Z","signature_b64":"FUN28cCGhlXiDoZ2XcbLIncXidiEj9oyyVwDT4etb74BPxsa1LVbZPmniav6OQiAxrPoo34linaqIoRe/n94Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"658f2bde950299deb6b7ee57fee297091237c0cee1249650629f3af873eb93f5","last_reissued_at":"2026-07-08T01:18:40.649276Z","signature_status":"signed_v1","first_computed_at":"2026-07-08T01:18:40.649276Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2607.05655","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":"2026-07-08T01:18:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1OAfvp9bbriYc1dmh7kgTO4N81AP5yK+dr85d/NGWMG/7lWxV1sbQgRL/HMEOpXkiMPRaR8eh+pmP0OAzGt0Dg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-20T02:29:18.396028Z"},"content_sha256":"e14c2755e350079682d8200f277424bdbb3232f3afb1ba4d32c25a4ee0a7ae87","schema_version":"1.0","event_id":"sha256:e14c2755e350079682d8200f277424bdbb3232f3afb1ba4d32c25a4ee0a7ae87"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:MWHSXXUVAKM55NVX5ZL75YUXBE","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"GeoXplain: On-the-Fly Visual Explanations for Weather Foundation Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.HC","authors_text":"Benedikt Soja, Christina Humer, Clemens Walter Koprolin, Leonardo Trentini, Mennatallah El-Assady","submitted_at":"2026-07-06T21:51:46Z","abstract_excerpt":"Weather and climate foundation models produce high-dimensional forecasts whose learned relationships are difficult to inspect with static plots alone. GeoXplain is an interactive Python-based visualization toolkit for exploring geospatial attribution maps across climate variables, atmospheric pressure levels, and forecast time. The toolkit accepts attribution bundles containing attribution grids together with corresponding metadata and renders them in a notebook widget or browser with map and globe modes, linked timelines, pressure-level controls, target annotations, and optional physical-fiel"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.05655","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/2607.05655/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":"2026-07-08T01:18:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EisHJASZ+dW0Y/wQ35Vy8AnrksUymnR3TblryDlFiXJv3PX1A/ds4oKNEhub+8q7ZgmXW+Uu34vbLSRr5ShtDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-20T02:29:18.396602Z"},"content_sha256":"67fe6c2853320f95b30d7ced2d04923f38b8edd32de772a2c4fa8d66bb8029e6","schema_version":"1.0","event_id":"sha256:67fe6c2853320f95b30d7ced2d04923f38b8edd32de772a2c4fa8d66bb8029e6"},{"event_type":"integrity_finding","subject_pith_number":"pith:2026:MWHSXXUVAKM55NVX5ZL75YUXBE","target":"integrity","payload":{"note":"DOI in the printed bibliography is fragmented by whitespace or line breaks. A longer candidate (10.1109/MCSE.2021.30521011) was visible in the surrounding text but could not be confirmed against doi.org as printed.","snippet":"M. Beg, J. Taka, T. Kluyver, A. Konovalov, M. Ragan-Kelley, N. M. Thiéry et al. Using Jupyter for reproducible scientific workflows.Computing in Science & Engineering, 23(2):36–46, 2021. doi: 10.1109/MCSE.2021. 3052101 1","arxiv_id":"2607.05655","detector":"doi_compliance","evidence":{"ref_index":3,"verdict_class":"incontrovertible","resolved_title":null,"printed_excerpt":"M. Beg, J. Taka, T. Kluyver, A. Konovalov, M. Ragan-Kelley, N. M. Thiéry et al. Using Jupyter for reproducible scientific workflows.Computing in Science & Engineering, 23(2):36–46, 2021. doi: 10.1109/MCSE.2021. 3052101 1","reconstructed_doi":"10.1109/MCSE.2021.30521011"},"severity":"advisory","ref_index":3,"audited_at":"2026-07-11T04:25:30.067131Z","event_type":"pith.integrity.v1","detected_doi":"10.1109/MCSE.2021.30521011","detector_url":"https://pith.science/pith-integrity-protocol#doi_compliance","external_url":null,"finding_type":"recoverable_identifier","evidence_hash":"4268153b37fa144451272c725459808b4a96f149de47027a21c9557fbe33e528","paper_version":1,"verdict_class":"incontrovertible","resolved_title":null,"detector_version":"1.0.0","detected_arxiv_id":null,"integrity_event_id":12102,"payload_sha256":"43f1fd452bc48951c250c15e97b89c86068cd829729346463d959a5ce9188529","signature_b64":"DmfA3xATSK9lxl/9BJGUXn2Z2epQckH6A8EzRtkB4w4WO6to3WOSKu+C1dpSh4BuwBqnOpGm/QrvJ2V23teYAg==","signing_key_id":"pith-v1-2026-05"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-11T04:25:35Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"7ahCVQijIidWPXjoen7+2cQRGzAYaswhT6fy1kloN5CU72r/pSgGGgDfJaLCVtsoDdpBFJD21tUk9QwNIA4IAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-20T02:29:18.414524Z"},"content_sha256":"694b68127d062177443fde0b7c596a6d46369994952704535dd06a739794ed76","schema_version":"1.0","event_id":"sha256:694b68127d062177443fde0b7c596a6d46369994952704535dd06a739794ed76"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/MWHSXXUVAKM55NVX5ZL75YUXBE/bundle.json","state_url":"https://pith.science/pith/MWHSXXUVAKM55NVX5ZL75YUXBE/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/MWHSXXUVAKM55NVX5ZL75YUXBE/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":"2026-08-20T02:29:18Z","links":{"resolver":"https://pith.science/pith/MWHSXXUVAKM55NVX5ZL75YUXBE","bundle":"https://pith.science/pith/MWHSXXUVAKM55NVX5ZL75YUXBE/bundle.json","state":"https://pith.science/pith/MWHSXXUVAKM55NVX5ZL75YUXBE/state.json","well_known_bundle":"https://pith.science/.well-known/pith/MWHSXXUVAKM55NVX5ZL75YUXBE/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:MWHSXXUVAKM55NVX5ZL75YUXBE","merge_version":"pith-open-graph-merge-v1","event_count":3,"valid_event_count":3,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"b44cd2c1ef7e9870d6ee60fef6b1f32f685d5687a1a716d4a8efb6275d3efe10","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.HC","submitted_at":"2026-07-06T21:51:46Z","title_canon_sha256":"0af84674c51e8a328acc1ad47c7067199d55f2687e6c82b653cd069bc6f729f5"},"schema_version":"1.0","source":{"id":"2607.05655","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.05655","created_at":"2026-07-08T01:18:40Z"},{"alias_kind":"arxiv_version","alias_value":"2607.05655v1","created_at":"2026-07-08T01:18:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.05655","created_at":"2026-07-08T01:18:40Z"},{"alias_kind":"pith_short_12","alias_value":"MWHSXXUVAKM5","created_at":"2026-07-08T01:18:40Z"},{"alias_kind":"pith_short_16","alias_value":"MWHSXXUVAKM55NVX","created_at":"2026-07-08T01:18:40Z"},{"alias_kind":"pith_short_8","alias_value":"MWHSXXUV","created_at":"2026-07-08T01:18:40Z"}],"graph_snapshots":[{"event_id":"sha256:67fe6c2853320f95b30d7ced2d04923f38b8edd32de772a2c4fa8d66bb8029e6","target":"graph","created_at":"2026-07-08T01:18:40Z","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/2607.05655/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Weather and climate foundation models produce high-dimensional forecasts whose learned relationships are difficult to inspect with static plots alone. GeoXplain is an interactive Python-based visualization toolkit for exploring geospatial attribution maps across climate variables, atmospheric pressure levels, and forecast time. The toolkit accepts attribution bundles containing attribution grids together with corresponding metadata and renders them in a notebook widget or browser with map and globe modes, linked timelines, pressure-level controls, target annotations, and optional physical-fiel","authors_text":"Benedikt Soja, Christina Humer, Clemens Walter Koprolin, Leonardo Trentini, Mennatallah El-Assady","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.HC","submitted_at":"2026-07-06T21:51:46Z","title":"GeoXplain: On-the-Fly Visual Explanations for Weather Foundation Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.05655","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:e14c2755e350079682d8200f277424bdbb3232f3afb1ba4d32c25a4ee0a7ae87","target":"record","created_at":"2026-07-08T01:18:40Z","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":"b44cd2c1ef7e9870d6ee60fef6b1f32f685d5687a1a716d4a8efb6275d3efe10","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.HC","submitted_at":"2026-07-06T21:51:46Z","title_canon_sha256":"0af84674c51e8a328acc1ad47c7067199d55f2687e6c82b653cd069bc6f729f5"},"schema_version":"1.0","source":{"id":"2607.05655","kind":"arxiv","version":1}},"canonical_sha256":"658f2bde950299deb6b7ee57fee297091237c0cee1249650629f3af873eb93f5","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"658f2bde950299deb6b7ee57fee297091237c0cee1249650629f3af873eb93f5","first_computed_at":"2026-07-08T01:18:40.649276Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-08T01:18:40.649276Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"FUN28cCGhlXiDoZ2XcbLIncXidiEj9oyyVwDT4etb74BPxsa1LVbZPmniav6OQiAxrPoo34linaqIoRe/n94Cg==","signature_status":"signed_v1","signed_at":"2026-07-08T01:18:40.649743Z","signed_message":"canonical_sha256_bytes"},"source_id":"2607.05655","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e14c2755e350079682d8200f277424bdbb3232f3afb1ba4d32c25a4ee0a7ae87","sha256:67fe6c2853320f95b30d7ced2d04923f38b8edd32de772a2c4fa8d66bb8029e6","sha256:694b68127d062177443fde0b7c596a6d46369994952704535dd06a739794ed76"],"state_sha256":"edd4cc54d2bbf0cc1ecc460f4953370c886f0bdce4172e38ae322061cd8e2974"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"uuqodpfVK0CuoX97uk6bOIoYt2FrcMmW3ojqP9o04Grfb/Qa54XoPHQ5Dx+cxtIHCbACJs2hjsCnB5CRrug7Cw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-20T02:29:18.416744Z","bundle_sha256":"ac032337f302ef9c3283eb33d85c23ea57565c77fb1fc204d9372a2d5298d47f"}}