{"id":"W2584408557","doi":"10.1101/104190","title":"Fiber tractography using machine learning","year":2017,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"NIH Blueprint for Neuroscience Research; McDonnell Center for Systems Neuroscience; National Institutes of Health; Deutsche Forschungsgemeinschaft","keywords":"Tractography; Computer science; Random forest; Artificial intelligence; Imaging phantom; Fiber; Diffusion MRI; Machine learning; Pattern recognition (psychology); Physics; Medicine; Chemistry","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":[0.001870622,0.000923841,0.0009979303,0.002482801,0.0006284707,0.001867634,0.00123123,0.001484637,0.004441678],"category_scores_gemma":[0.004898746,0.0005105496,0.001163736,0.001566162,0.0008102364,0.001307976,0.001246779,0.001347334,0.002595618],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000848863,"about_ca_system_score_gemma":0.001322832,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003719245,"about_ca_topic_score_gemma":0.00483746,"domain_scores_codex":[0.9990637,0.000277201,0.00004767572,0.000276025,0.0002694913,0.00006599302],"domain_scores_gemma":[0.9980716,0.0006676072,0.00026301,0.000464997,0.0004448703,0.00008784913],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001707024,0.00007967733,0.00192857,0.0002816046,0.0002285968,0.000190292,0.0001124842,0.4684834,0.03998336,0.03141893,0.007689569,0.4494328],"study_design_scores_gemma":[0.000006874031,0.00002000432,0.0002921946,0.00001546935,0.000008676192,0.00006423139,0.000004998385,0.9776072,0.005723105,0.01378519,0.00245952,0.00001248331],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002755806,0.0001217654,0.9950831,0.00007159149,0.00002276546,0.00003218047,0.0001000202,0.001397638,0.000415194],"genre_scores_gemma":[0.1367401,0.0002734134,0.8586673,0.00007691495,0.00008888912,0.0001196038,0.0007664798,0.0006831579,0.002584167],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004441678,"threshold_uncertainty_score":0.0148589,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0603398396589757,"score_gpt":0.3155699989317103,"score_spread":0.2552301592727346,"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."}}