{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:5ICBSBBA7FAL25LN5BJKFG6RVI","short_pith_number":"pith:5ICBSBBA","schema_version":"1.0","canonical_sha256":"ea04190420f940bd756de852a29bd1aa29373b2b8dcef794baf19237a32277b3","source":{"kind":"arxiv","id":"2605.21307","version":1},"attestation_state":"computed","paper":{"title":"The Bayesian Gaussian Process Latent Variable Model for Spatio-Temporal Stream Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Marno Basson, Theresa R. Smith, Tobias M. Louw","submitted_at":"2026-05-20T15:35:35Z","abstract_excerpt":"A variational inference-based framework for training a multi-output Gaussian process latent variable model, specifically tailored to the tails-up spatio-temporal stream network, is developed. Training, given a censored observational data set subject to missing values, proceeds by maximising a secondary variational lower bound on the model log marginal likelihood using gradient-based optimisation. Consequently, the theoretical development for a new family of tails-up spatio-temporal stream network models is introduced which rely on the sparse Gaussian process inducing variable framework, the Ba"},"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":"2605.21307","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2026-05-20T15:35:35Z","cross_cats_sorted":[],"title_canon_sha256":"da85f8753d56133a9f44b3ba092e6612a8ab7d50bbbd76a5fb10a8eacba42842","abstract_canon_sha256":"9e98edf082fb4d4248af83b5b2d44269f0e931e7cedc476e7a8b14adb9ff5d46"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-21T02:05:28.366377Z","signature_b64":"Ab202FMqUpTo2R5/LPUeymIp0aElZJeKx9HKvzcgrruyNFc2eZ7RRDloZPOG/pKQ68oA95RISXTe/nXf89L7DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ea04190420f940bd756de852a29bd1aa29373b2b8dcef794baf19237a32277b3","last_reissued_at":"2026-05-21T02:05:28.365610Z","signature_status":"signed_v1","first_computed_at":"2026-05-21T02:05:28.365610Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"The Bayesian Gaussian Process Latent Variable Model for Spatio-Temporal Stream Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Marno Basson, Theresa R. Smith, Tobias M. Louw","submitted_at":"2026-05-20T15:35:35Z","abstract_excerpt":"A variational inference-based framework for training a multi-output Gaussian process latent variable model, specifically tailored to the tails-up spatio-temporal stream network, is developed. Training, given a censored observational data set subject to missing values, proceeds by maximising a secondary variational lower bound on the model log marginal likelihood using gradient-based optimisation. Consequently, the theoretical development for a new family of tails-up spatio-temporal stream network models is introduced which rely on the sparse Gaussian process inducing variable framework, the Ba"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.21307","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/2605.21307/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":"2605.21307","created_at":"2026-05-21T02:05:28.365741+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.21307v1","created_at":"2026-05-21T02:05:28.365741+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.21307","created_at":"2026-05-21T02:05:28.365741+00:00"},{"alias_kind":"pith_short_12","alias_value":"5ICBSBBA7FAL","created_at":"2026-05-21T02:05:28.365741+00:00"},{"alias_kind":"pith_short_16","alias_value":"5ICBSBBA7FAL25LN","created_at":"2026-05-21T02:05:28.365741+00:00"},{"alias_kind":"pith_short_8","alias_value":"5ICBSBBA","created_at":"2026-05-21T02:05:28.365741+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/5ICBSBBA7FAL25LN5BJKFG6RVI","json":"https://pith.science/pith/5ICBSBBA7FAL25LN5BJKFG6RVI.json","graph_json":"https://pith.science/api/pith-number/5ICBSBBA7FAL25LN5BJKFG6RVI/graph.json","events_json":"https://pith.science/api/pith-number/5ICBSBBA7FAL25LN5BJKFG6RVI/events.json","paper":"https://pith.science/paper/5ICBSBBA"},"agent_actions":{"view_html":"https://pith.science/pith/5ICBSBBA7FAL25LN5BJKFG6RVI","download_json":"https://pith.science/pith/5ICBSBBA7FAL25LN5BJKFG6RVI.json","view_paper":"https://pith.science/paper/5ICBSBBA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.21307&json=true","fetch_graph":"https://pith.science/api/pith-number/5ICBSBBA7FAL25LN5BJKFG6RVI/graph.json","fetch_events":"https://pith.science/api/pith-number/5ICBSBBA7FAL25LN5BJKFG6RVI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5ICBSBBA7FAL25LN5BJKFG6RVI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5ICBSBBA7FAL25LN5BJKFG6RVI/action/storage_attestation","attest_author":"https://pith.science/pith/5ICBSBBA7FAL25LN5BJKFG6RVI/action/author_attestation","sign_citation":"https://pith.science/pith/5ICBSBBA7FAL25LN5BJKFG6RVI/action/citation_signature","submit_replication":"https://pith.science/pith/5ICBSBBA7FAL25LN5BJKFG6RVI/action/replication_record"}},"created_at":"2026-05-21T02:05:28.365741+00:00","updated_at":"2026-05-21T02:05:28.365741+00:00"}