{"id":"W7010018281","doi":"","title":"Finding and explaining relations in a biographical knowledge graph based on life events : Case BiographySampo","year":2023,"lang":"en","type":"article","venue":"Aaltodoc (Aalto University)","topic":"Advanced Graph Neural Networks","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"China Scholarship Council","keywords":"Nucleofection; Gestational period; TSG101; Dysgeusia; Liquation; Diafiltration; Emperipolesis; Triacetin; Demotion","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002571746,0.0002281322,0.0002205608,0.004425881,0.0004469574,0.00004516833,0.0004729545,0.000135632,0.000005665301],"category_scores_gemma":[0.00006608648,0.0002602846,0.0001530205,0.01183417,0.0001159194,0.0006062078,0.0002969143,0.0003931299,0.00002857827],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004683704,"about_ca_system_score_gemma":0.00005926512,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005238535,"about_ca_topic_score_gemma":0.0001794171,"domain_scores_codex":[0.9982117,0.0002048228,0.0002056051,0.0006726021,0.0001870371,0.0005182225],"domain_scores_gemma":[0.998466,0.0006470347,0.0000954835,0.0004478901,0.00004507698,0.0002985504],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002838886,0.0007804977,0.5175179,0.00011196,0.0001780361,0.02270653,0.00409685,0.03689318,0.0002850833,0.3859279,0.003622392,0.02759575],"study_design_scores_gemma":[0.008719344,0.001186852,0.2457863,0.0009407664,0.0001014899,0.0003206367,0.001848241,0.6938357,0.0001123988,0.01958865,0.02487226,0.002687377],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9302243,0.0001042651,0.06335241,0.001334735,0.0004517619,0.0004905575,0.00001700249,0.0009495963,0.003075403],"genre_scores_gemma":[0.9958732,0.00009474631,0.003703733,0.0001403622,0.00002311486,0.000003250522,0.000009177314,0.0000170274,0.0001354229],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6569425,"threshold_uncertainty_score":0.9999849,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0271971295839314,"score_gpt":0.2491546237887301,"score_spread":0.2219574942047987,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}