{"id":"W2222869326","doi":"10.1016/j.media.2015.10.011","title":"Strengths and weaknesses of state of the art fiber tractography pipelines – A comprehensive in-vivo and phantom evaluation study using Tractometer","year":2015,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":74,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Sherbrooke","funders":"National Institute of Mental Health; Deutsche Forschungsgemeinschaft","keywords":"Tractography; Metric (unit); Imaging phantom; Computer science; Consistency (knowledge bases); Artificial intelligence; Machine learning; Data mining; Diffusion MRI; Magnetic resonance imaging; Engineering; Medicine; Nuclear medicine; Radiology","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":[0.0003916479,0.00009217909,0.0003734046,0.0002940606,0.00002006008,0.00000705835,0.00006225934,0.00002645046,0.00005461774],"category_scores_gemma":[0.0005491016,0.00006056178,0.00007951073,0.001123838,0.000239598,0.00007687042,0.0000514627,0.0001212452,1.427647e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009499494,"about_ca_system_score_gemma":0.0000523053,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001115261,"about_ca_topic_score_gemma":0.00006243259,"domain_scores_codex":[0.9986913,0.0001074503,0.000350781,0.0001957094,0.0005666068,0.00008820912],"domain_scores_gemma":[0.9989288,0.0002460103,0.0001587675,0.0002378561,0.0003315296,0.00009702695],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002933931,0.00375003,0.7598091,0.00024872,0.001626896,0.00005430455,0.003806707,0.0002916858,0.08371328,0.0000064543,0.0006714133,0.145728],"study_design_scores_gemma":[0.007877582,0.0006452349,0.7544301,0.0004009869,0.01392914,0.00008478909,0.003848162,0.1793668,0.03537626,0.001378474,0.002204909,0.0004575694],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9958,0.0002461205,0.003130184,0.0003692986,0.000006920985,0.0003690502,0.00001169212,0.00001068139,0.00005602697],"genre_scores_gemma":[0.9967418,0.000109089,0.003021753,0.0000656675,0.00001047873,0.00001549117,0.000004586363,0.000007024459,0.00002405271],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1790751,"threshold_uncertainty_score":0.2469638,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0852731398949972,"score_gpt":0.4298637285706721,"score_spread":0.3445905886756749,"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."}}