{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:M47DCPK22OSWFSY4UQRC3VW4JE","short_pith_number":"pith:M47DCPK2","schema_version":"1.0","canonical_sha256":"673e313d5ad3a562cb1ca4222dd6dc490f2b7036a91be0e5cc5fcd969fb190f9","source":{"kind":"arxiv","id":"2606.07725","version":1},"attestation_state":"computed","paper":{"title":"GNSS-FM: A Self-Supervised Foundation Model for Daily GNSS Displacement Time Series","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"physics.geo-ph","authors_text":"(2) ETH AI Center, Benedikt Soja (1) ((1) Institute of Geodesy, ETH Zurich, Fanny Lehmann (2), Laura Crocetti (1), Leonardo Trentini (1), Nick Teutschmann (1), Photogrammetry, Switzerland, Switzerland)","submitted_at":"2026-06-05T16:39:49Z","abstract_excerpt":"Displacement time series from Global Navigation Satellite Systems (GNSS) are essential for a wide range of applications, including monitoring tectonic crustal deformations and investigating the different stages of the earthquake cycle. Machine learning methods have proven promising for GNSS applications; however, most remain fully supervised. This creates a bottleneck as labeled data are scarce, even though large amounts of unlabeled GNSS data are freely available. We present GNSS-FM, a self-supervised foundation model for daily GNSS time series. The model uses a dual-stream input combining di"},"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":"2606.07725","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"physics.geo-ph","submitted_at":"2026-06-05T16:39:49Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"3f82cae6228029050c73ccf592604bcd07d5a4b57e7b8d3eadd6b63bfb105a7f","abstract_canon_sha256":"f1135b1e5007c301b13a095bc324c6b4304f9934efb02e36bfa4395e9f270924"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-09T01:04:50.452036Z","signature_b64":"C98gvt/jYEuXY1DGg96lG9vNPoW8j74yDe5pTB9tv84+7JF5M78CC0GqE3EAkr5y5DbreJL5xBcFxWfeBBZqCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"673e313d5ad3a562cb1ca4222dd6dc490f2b7036a91be0e5cc5fcd969fb190f9","last_reissued_at":"2026-06-09T01:04:50.451619Z","signature_status":"signed_v1","first_computed_at":"2026-06-09T01:04:50.451619Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"GNSS-FM: A Self-Supervised Foundation Model for Daily GNSS Displacement Time Series","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"physics.geo-ph","authors_text":"(2) ETH AI Center, Benedikt Soja (1) ((1) Institute of Geodesy, ETH Zurich, Fanny Lehmann (2), Laura Crocetti (1), Leonardo Trentini (1), Nick Teutschmann (1), Photogrammetry, Switzerland, Switzerland)","submitted_at":"2026-06-05T16:39:49Z","abstract_excerpt":"Displacement time series from Global Navigation Satellite Systems (GNSS) are essential for a wide range of applications, including monitoring tectonic crustal deformations and investigating the different stages of the earthquake cycle. Machine learning methods have proven promising for GNSS applications; however, most remain fully supervised. This creates a bottleneck as labeled data are scarce, even though large amounts of unlabeled GNSS data are freely available. We present GNSS-FM, a self-supervised foundation model for daily GNSS time series. The model uses a dual-stream input combining di"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.07725","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/2606.07725/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2606.07725","created_at":"2026-06-09T01:04:50.451683+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.07725v1","created_at":"2026-06-09T01:04:50.451683+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.07725","created_at":"2026-06-09T01:04:50.451683+00:00"},{"alias_kind":"pith_short_12","alias_value":"M47DCPK22OSW","created_at":"2026-06-09T01:04:50.451683+00:00"},{"alias_kind":"pith_short_16","alias_value":"M47DCPK22OSWFSY4","created_at":"2026-06-09T01:04:50.451683+00:00"},{"alias_kind":"pith_short_8","alias_value":"M47DCPK2","created_at":"2026-06-09T01:04:50.451683+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/M47DCPK22OSWFSY4UQRC3VW4JE","json":"https://pith.science/pith/M47DCPK22OSWFSY4UQRC3VW4JE.json","graph_json":"https://pith.science/api/pith-number/M47DCPK22OSWFSY4UQRC3VW4JE/graph.json","events_json":"https://pith.science/api/pith-number/M47DCPK22OSWFSY4UQRC3VW4JE/events.json","paper":"https://pith.science/paper/M47DCPK2"},"agent_actions":{"view_html":"https://pith.science/pith/M47DCPK22OSWFSY4UQRC3VW4JE","download_json":"https://pith.science/pith/M47DCPK22OSWFSY4UQRC3VW4JE.json","view_paper":"https://pith.science/paper/M47DCPK2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.07725&json=true","fetch_graph":"https://pith.science/api/pith-number/M47DCPK22OSWFSY4UQRC3VW4JE/graph.json","fetch_events":"https://pith.science/api/pith-number/M47DCPK22OSWFSY4UQRC3VW4JE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/M47DCPK22OSWFSY4UQRC3VW4JE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/M47DCPK22OSWFSY4UQRC3VW4JE/action/storage_attestation","attest_author":"https://pith.science/pith/M47DCPK22OSWFSY4UQRC3VW4JE/action/author_attestation","sign_citation":"https://pith.science/pith/M47DCPK22OSWFSY4UQRC3VW4JE/action/citation_signature","submit_replication":"https://pith.science/pith/M47DCPK22OSWFSY4UQRC3VW4JE/action/replication_record"}},"created_at":"2026-06-09T01:04:50.451683+00:00","updated_at":"2026-06-09T01:04:50.451683+00:00"}