{"id":"W3029385835","doi":"10.1371/journal.pone.0233645","title":"Freewater estimatoR using iNtErpolated iniTialization (FERNET): Characterizing peritumoral edema using clinically feasible diffusion MRI data","year":2020,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"Synaptive (Canada)","funders":"National Institute of Neurological Disorders and Stroke; National Institute of Mental Health; National Institutes of Health","keywords":"Initialization; Diffusion MRI; Estimator; Tractography; Computer science; Magnetic resonance imaging; Edema; Compartment (ship); Medicine; Radiology; Biomedical engineering; Algorithm; Mathematics; Surgery; Statistics","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":[0.001970891,0.001046554,0.0007734803,0.0011208,0.0005290752,0.0008891806,0.001045091,0.001595286,0.001131071],"category_scores_gemma":[0.008281704,0.0004312548,0.0007192547,0.000811717,0.0006506342,0.001536376,0.001319784,0.001324743,0.0005539263],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000506458,"about_ca_system_score_gemma":0.001561535,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004104486,"about_ca_topic_score_gemma":0.006658042,"domain_scores_codex":[0.9996561,0.0001174527,0.00002339899,0.0000885608,0.00007461437,0.00003989369],"domain_scores_gemma":[0.9986672,0.0007433721,0.0001692792,0.000144041,0.0002076068,0.00006845493],"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":[0.0008946888,0.0002266938,0.005595483,0.0004956552,0.0001751927,0.0004870573,0.000386171,0.4157325,0.0690442,0.01297955,0.007423798,0.486559],"study_design_scores_gemma":[0.00004803768,0.00009125588,0.0009288511,0.00004139385,0.00003031005,0.0002310812,0.00003424397,0.963654,0.02520078,0.005829072,0.003868091,0.0000429775],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01409624,0.0002846647,0.9840867,0.0001066806,0.00003719481,0.00004897631,0.00009851607,0.0008847121,0.0003562417],"genre_scores_gemma":[0.1868946,0.0004204761,0.8092877,0.000161882,0.00004643422,0.0001647735,0.0008464635,0.0004678647,0.001709805],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004104486,"threshold_uncertainty_score":0.01042318,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4784936936358914,"score_gpt":0.4139731804885342,"score_spread":0.06452051314735724,"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."}}