{"id":"W4391322151","doi":"10.1111/aogs.14772","title":"Artificial intelligence/machine learning and journalology: Challenges and opportunities","year":2024,"lang":"en","type":"editorial","venue":"Acta Obstetricia Et Gynecologica Scandinavica","topic":"Artificial Intelligence in Healthcare and Education","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Mount Sinai Hospital","funders":"","keywords":"Publishing; Mainstream; Introspection; Quality (philosophy); Artificial intelligence; Computer science; Psychology; Political science; Law; Epistemology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch","scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.02734884,0.0007210617,0.001452927,0.004743587,0.00752816,0.03492386,0.002518278,0.01087885,0.009249782],"category_scores_gemma":[0.04588223,0.0007457248,0.0008203556,0.003995368,0.02257779,0.03078579,0.00673496,0.01515823,0.003992377],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004667749,"about_ca_system_score_gemma":0.01130324,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001293279,"about_ca_topic_score_gemma":0.002505392,"domain_scores_codex":[0.9828993,0.008289583,0.001596777,0.001367012,0.004647587,0.001199752],"domain_scores_gemma":[0.8972394,0.0620514,0.005261869,0.005770144,0.01654077,0.01313643],"domain_codex":null,"domain_gemma":"evaluation","domain_candidate":"evaluation","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006779186,0.00006778003,0.0007358165,0.001186488,0.00002898519,0.00028997,0.003917216,0.0002044062,0.0003583881,0.4187049,0.4534517,0.1209867],"study_design_scores_gemma":[0.00001080306,0.00002516541,0.0004039698,0.0009443773,0.000008919322,0.0002966587,0.003835306,0.0002873842,0.0001099334,0.1636755,0.830357,0.00004495461],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"editorial","genre_scores_codex":[0.001312924,0.1304589,0.002938587,0.7397397,0.1089457,0.00001688199,0.00005293279,0.0001316075,0.0164027],"genre_scores_gemma":[0.07157456,0.2294378,0.01005321,0.1878929,0.4778713,0.00008423445,0.0001281786,0.0004921862,0.02246565],"genre_candidate":"editorial","genre_consensus":null,"teacher_disagreement_score":0.9726512,"threshold_uncertainty_score":0.1446363,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.211226811959128,"score_gpt":0.4114383140048328,"score_spread":0.2002115020457048,"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."}}