{"id":"W4399441519","doi":"10.34133/hds.0112","title":"2023 Beijing Health Data Science Summit","year":2024,"lang":"en","type":"article","venue":"Health Data Science","topic":"Artificial Intelligence in Healthcare","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Summit; Beijing; Health science; Political science; Geography; China; Medicine; Cartography; Medical education","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","sts","open_science","insufficient_payload"],"consensus_categories":["sts","open_science"],"category_scores_codex":[0.04886319,0.0003768348,0.0006333417,0.001072394,0.01215218,0.0004405235,0.01329528,0.0001501326,0.0006068828],"category_scores_gemma":[0.006471212,0.0003380675,0.00003186063,0.009010565,0.003380562,0.01023549,0.01040777,0.002183396,0.004886656],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002501567,"about_ca_system_score_gemma":0.06746699,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.03507874,"about_ca_topic_score_gemma":0.03257308,"domain_scores_codex":[0.9856997,0.0007883878,0.002299098,0.004009061,0.00290003,0.004303759],"domain_scores_gemma":[0.9856766,0.001566119,0.0006070674,0.009241738,0.0006287432,0.00227974],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004906789,0.0001679097,0.03258403,0.007056533,0.00001375117,0.0000638795,0.0114868,0.00003784606,0.0003725216,0.1042513,0.4569582,0.3869582],"study_design_scores_gemma":[0.0001120176,0.0002705272,0.007010687,0.004192855,0.000008686957,0.0000172988,0.01402263,0.220156,0.0000400086,0.001137897,0.7524125,0.0006189388],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.06895019,0.076107,0.02614439,0.6525202,0.09154099,0.01889696,0.03135131,0.005544331,0.02894466],"genre_scores_gemma":[0.9014683,0.01315548,0.02263364,0.05334997,0.004144612,0.0002146285,0.002619556,0.0001954875,0.0022183],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8325182,"threshold_uncertainty_score":0.9999071,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6741552806170339,"score_gpt":0.6558769739566774,"score_spread":0.01827830666035646,"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."}}