{"id":"W2849179291","doi":"10.1038/s41598-018-31911-7","title":"Objective Evaluation of Multiple Sclerosis Lesion Segmentation using a Data Management and Processing Infrastructure","year":2018,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Multiple Sclerosis Research Studies","field":"Medicine","cited_by":282,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; University of Guelph; Concordia University","funders":"Agence Nationale de la Recherche","keywords":"Computer science; Segmentation; Artificial intelligence; Protocol (science); Machine learning; Deep learning; Multiple sclerosis; Data mining; Range (aeronautics); Data science; Medicine; Pathology","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":[0.003101007,0.000109392,0.0001842046,0.0002603139,0.0004103196,0.0001008469,0.00008120418,0.00003917158,0.00003969932],"category_scores_gemma":[0.0007163534,0.00008957653,0.00002214253,0.0005337303,0.0005310357,0.0003678624,0.0004045818,0.0000661182,0.000001131318],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001567897,"about_ca_system_score_gemma":0.0001900206,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004816673,"about_ca_topic_score_gemma":0.00003929785,"domain_scores_codex":[0.9971747,0.00008164226,0.0003805741,0.000721266,0.001443588,0.0001982637],"domain_scores_gemma":[0.9977365,0.0000222564,0.0003042569,0.0007652045,0.00109556,0.00007618073],"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":[0.00007206432,0.00008792289,0.08971532,0.0003395175,0.00008937652,0.000009459816,0.001979626,0.00008425066,0.5991279,8.785485e-7,0.001666352,0.3068273],"study_design_scores_gemma":[0.001246721,0.00007938029,0.7621619,0.0008447246,0.0003422197,0.00005483533,0.00237402,0.1618465,0.07025821,0.0003571586,0.0002997119,0.0001345934],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9960985,0.000277308,0.001293809,0.00002844025,0.0005963731,0.0011877,0.000007368234,0.00002235987,0.0004881231],"genre_scores_gemma":[0.981661,0.00003089178,0.01804076,0.000007423037,0.00005847683,0.000018665,0.0001056098,0.00001076352,0.00006636434],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6724466,"threshold_uncertainty_score":0.3652825,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2296485877532672,"score_gpt":0.404134231619972,"score_spread":0.1744856438667048,"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."}}