{"id":"W4392907840","doi":"10.32920/25412641.v1","title":"Fetal Organ Anomaly Identification, Segmentation, and Optimized Data Augmentation","year":2024,"lang":"en","type":"preprint","venue":"","topic":"Fetal and Pediatric Neurological Disorders","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Segmentation; Pipeline (software); Computer science; Artificial intelligence; Identification (biology); Magnetic resonance imaging; Pattern recognition (psychology); Image segmentation; Medicine; Radiology; Biology","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":[],"consensus_categories":[],"category_scores_codex":[0.0007082978,0.0008372348,0.0004966313,0.0006069515,0.0002986883,0.0007543435,0.0009163342,0.0007627175,0.00141078],"category_scores_gemma":[0.001869754,0.0003942385,0.0008149314,0.0005453455,0.0005169176,0.0007727923,0.001213604,0.00105155,0.000720182],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003820367,"about_ca_system_score_gemma":0.0008309306,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001839784,"about_ca_topic_score_gemma":0.002410147,"domain_scores_codex":[0.9996443,0.00006483676,0.00002173421,0.0001192688,0.0001030937,0.00004685836],"domain_scores_gemma":[0.9995108,0.0001805272,0.00006021979,0.0001102416,0.0001108604,0.00002748573],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003520542,0.000120908,0.003870323,0.0001635846,0.00006827224,0.0001729847,0.0002222954,0.1264214,0.2012704,0.005130896,0.002959891,0.659247],"study_design_scores_gemma":[0.0000081693,0.0001265046,0.002538877,0.00001812814,0.00003038466,0.0002669884,0.00004868652,0.8807386,0.1080497,0.004010655,0.004139971,0.00002333097],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04989527,0.0003645534,0.9457028,0.0002896669,0.00005257908,0.00006382853,0.0001952224,0.002410584,0.001025395],"genre_scores_gemma":[0.3576415,0.000440844,0.6375478,0.0001875999,0.00006243762,0.0001563292,0.0009140199,0.0003798208,0.002669796],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001839784,"threshold_uncertainty_score":0.004719555,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04533896798972931,"score_gpt":0.3232794276464568,"score_spread":0.2779404596567275,"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."}}