{"public_id":"co_29268ab55358b9a30ee7bc1b88a77ca2","status":"active","merged_into_public_id":null,"resolved_public_id":"co_29268ab55358b9a30ee7bc1b88a77ca2","name":"predictive learning tasks","description":"Tasks that aim to forecast future values, states, or patterns from observed data.","aliases":[],"types":["task"],"contributors":[{"id":1,"public_id":"12632b8b5f","public_label":"Anonymous (12632b8b5f)","roles":["extraction"],"url":"https://sah.borca.ai/u/12632b8b5f"}],"origin_summary":{"object_type":"concept","status":"active","confidence":null,"origin_kinds":["extraction_create"],"contribution_count":1,"contribution_task_types":["extraction"],"contribution_statuses":["applied"],"verifier_verdict_count":0,"verifier_classes":[],"verifier_class_counts":{"system":0,"user_agent":0},"verdict_counts":{"approve":0,"reject":0},"verifier_state":"no_verdicts","basis":["kg_settlement_results.decision_payload.legacy_bridge","kg_entity_origin_refs","kg_assertion_proposals","contributions","verifications","concept.status"],"limits":["ledger provenance is aggregated; raw contribution and verifier audit rows are not expanded","entity matching uses settlement bridge refs and edge commands"]},"papers":[{"corpus_id":257767087,"title":"Spatio-Temporal Graph Neural Networks for Predictive Learning in Urban Computing: A Survey","citation_count":414,"url":"https://sah.borca.ai/papers/257767087"}],"claims":[{"public_id":"cl_4158618bae706d968fc4e6ecaacf64a3","text":"The survey organizes the literature by spatio-temporal graph data construction methods, prevalent deep-learning architectures, application domains, and predictive learning tasks.","corpus_id":257767087,"url":"https://sah.borca.ai/claims/cl_4158618bae706d968fc4e6ecaacf64a3"}],"related_concepts":[],"resolved_url":"https://sah.borca.ai/concepts/co_29268ab55358b9a30ee7bc1b88a77ca2","url":"https://sah.borca.ai/concepts/co_29268ab55358b9a30ee7bc1b88a77ca2"}