{"id":"W2912344046","doi":"10.1017/cjn.2018.381","title":"Application of Automated Brain Segmentation and Fiber Tracking in Hemimegalencephaly","year":2019,"lang":"en","type":"letter","venue":"Canadian Journal of Neurological Sciences / Journal Canadien des Sciences Neurologiques","topic":"Fetal and Pediatric Neurological Disorders","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Segmentation; Content (measure theory); Hemimegalencephaly; Computer science; Artificial intelligence; Fiber; Natural language processing; Computer vision; Neuroscience; Psychology; Epilepsy; Mathematics; Chemistry","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.002490484,0.0003997693,0.0009122272,0.001473132,0.0005089329,0.0002225977,0.0009595638,0.000640025,0.0001337897],"category_scores_gemma":[0.001883899,0.0002722858,0.0002490677,0.001392994,0.004363598,0.0005666821,0.00005386181,0.002916261,0.000004287587],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001535952,"about_ca_system_score_gemma":0.002214735,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00139269,"about_ca_topic_score_gemma":0.005001737,"domain_scores_codex":[0.9954215,0.0007811813,0.001244313,0.000652126,0.0008625856,0.001038257],"domain_scores_gemma":[0.996321,0.001270483,0.001284345,0.0001500313,0.0003529634,0.0006211469],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002004688,0.0000595153,0.915033,0.000202049,0.00003101505,0.01059022,0.0002982226,0.001932997,0.0002039286,0.0000418434,0.06073452,0.01067227],"study_design_scores_gemma":[0.00266305,0.1767742,0.6372178,0.0004379021,0.0003873415,0.07027402,0.0002766771,0.01368603,0.0001921399,0.01105247,0.08559544,0.001442931],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8416722,0.001464022,0.000007983755,0.1551163,0.0003256846,0.0003528045,0.0000198042,0.00002041319,0.001020816],"genre_scores_gemma":[0.830953,0.0004721389,0.0006844748,0.1673519,0.0004460812,0.000004281719,0.000003484119,0.00001733977,0.00006729276],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2778152,"threshold_uncertainty_score":0.9999729,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02352423632801292,"score_gpt":0.280159774701867,"score_spread":0.2566355383738541,"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."}}