{"id":"W4402035616","doi":"10.32920/26882494","title":"Automated Deep Learning Detection Algorithms for Fetal Orientation and Placenta Previa","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Knowledge Management and Technology","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Placenta previa; Orientation (vector space); Computer science; Artificial intelligence; Placenta; Fetus; Algorithm; Obstetrics; Medicine; Pregnancy; Mathematics; Biology; Geometry","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":[],"consensus_categories":[],"category_scores_codex":[0.001501008,0.0002261212,0.0003214319,0.0007322094,0.0001707163,0.0006334044,0.00036986,0.0003408621,0.0001448912],"category_scores_gemma":[0.001023689,0.0001767762,0.000126293,0.0004697617,0.00007416245,0.000100238,0.001692951,0.0004635191,0.0003073908],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006969592,"about_ca_system_score_gemma":0.0000232097,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002009305,"about_ca_topic_score_gemma":0.0002141443,"domain_scores_codex":[0.9977258,0.00009593173,0.000538441,0.0009778917,0.000436005,0.0002259253],"domain_scores_gemma":[0.9987621,0.0003929225,0.0002297007,0.0003480062,0.0002181173,0.00004915295],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002169245,0.00001655143,0.0004376742,0.0001117233,0.00006481521,0.00000298459,0.0002804922,0.0001846754,0.0001094066,0.00194715,0.0009853214,0.9958375],"study_design_scores_gemma":[0.0003029177,0.0001088063,0.0005830059,0.00003546921,0.00009242153,0.000005276943,0.0008691848,0.8925126,0.0009690452,0.08758845,0.01671043,0.0002224339],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.179208,0.001690559,0.7907073,0.0007267516,0.005966719,0.002396323,0.00001473421,0.004437421,0.01485222],"genre_scores_gemma":[0.967186,0.00005894811,0.007780273,0.00001842869,0.0001597572,0.0002178667,0.00004604334,0.00003074394,0.02450193],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9956151,"threshold_uncertainty_score":0.7208726,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06761688158696937,"score_gpt":0.388664926546012,"score_spread":0.3210480449590427,"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."}}