{"id":"W2749609967","doi":"10.1002/nbm.3793","title":"User‐independent diffusion tensor imaging analysis pipelines in a rat model presenting ventriculomegalia: A comparison study","year":2017,"lang":"en","type":"article","venue":"NMR in Biomedicine","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Montreal Heart Institute; Université de Montréal; Centre Hospitalier Universitaire Sainte-Justine; Polytechnique Montréal","funders":"Institute of Human Development, Child and Youth Health; Fonds de Recherche du Québec - Santé; Canadian Institutes of Health Research","keywords":"Diffusion MRI; Tensor (intrinsic definition); Pipeline transport; Computer science; Geology; Magnetic resonance imaging; Mathematics; Chemistry; Medicine; Radiology; Geometry","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00145978,0.001007383,0.0007509771,0.001151937,0.0002620244,0.0006114338,0.0006781023,0.0007859812,0.0007704998],"category_scores_gemma":[0.001783789,0.0004314779,0.001076799,0.0003938006,0.0005953937,0.0008081571,0.0005160376,0.0008487001,0.0003818839],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00047624,"about_ca_system_score_gemma":0.0005946945,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002816734,"about_ca_topic_score_gemma":0.00290545,"domain_scores_codex":[0.9993927,0.0001105195,0.00007311001,0.0001478648,0.0001748527,0.000100763],"domain_scores_gemma":[0.9985577,0.0002143459,0.0002975632,0.0003403036,0.000417481,0.0001725379],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.01277791,0.006832368,0.01280754,0.001136884,0.001135646,0.0007833606,0.0007212033,0.01612652,0.853959,0.0007519742,0.001503146,0.09146449],"study_design_scores_gemma":[0.0008448376,0.1361628,0.07917178,0.000200678,0.002842398,0.0029239,0.0008871022,0.1046107,0.6638108,0.001057412,0.007006575,0.0004811161],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9876142,0.0006213813,0.01067072,0.00004811125,0.0000602204,0.0001284667,0.0003783861,0.0002290971,0.0002493323],"genre_scores_gemma":[0.9648933,0.00190374,0.02914357,0.00007819266,0.00004111921,0.0004412702,0.001414881,0.0002293389,0.001854585],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002816734,"threshold_uncertainty_score":0.007720113,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07894206902046202,"score_gpt":0.4211373113286327,"score_spread":0.3421952423081707,"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."}}