{"id":"W4306176749","doi":"10.32920/21287922","title":"Objective Evaluation of Multiple Sclerosis Lesion Segmentation using a Data Management and Processing Infrastructure","year":2022,"lang":"en","type":"preprint","venue":"","topic":"Digital Imaging for Blood Diseases","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; University of Guelph; Concordia University","funders":"Agence Nationale de la Recherche","keywords":"Segmentation; Computer science; Artificial intelligence; Deep learning; Protocol (science); Machine learning; Data mining; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01338144,0.001640816,0.0009394841,0.003196809,0.0008559182,0.003312799,0.002248532,0.001736245,0.001871574],"category_scores_gemma":[0.02426264,0.0004483286,0.0008074086,0.002097645,0.00111464,0.002620307,0.002553473,0.001073392,0.001339474],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002013561,"about_ca_system_score_gemma":0.001909362,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004026282,"about_ca_topic_score_gemma":0.003526514,"domain_scores_codex":[0.987326,0.002846304,0.001082785,0.00279483,0.005267872,0.0006821917],"domain_scores_gemma":[0.9790857,0.007258091,0.002221554,0.003136343,0.007115127,0.00118315],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.005443247,0.002811356,0.06321117,0.002748136,0.001429367,0.0007111848,0.001041007,0.2170423,0.08370349,0.006434766,0.04664768,0.5687762],"study_design_scores_gemma":[0.0004226393,0.003873843,0.07197491,0.0002193603,0.0002431582,0.0008466078,0.0006540952,0.7991788,0.1014634,0.003509885,0.01745478,0.0001585052],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7807664,0.002488931,0.1705179,0.001295743,0.0004764744,0.001282822,0.007319293,0.02330585,0.0125466],"genre_scores_gemma":[0.8505523,0.0003121671,0.1299934,0.0002393381,0.0001252497,0.0002851038,0.01530661,0.001115801,0.002070083],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01338144,"threshold_uncertainty_score":0.07076871,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1665053843283483,"score_gpt":0.3499285426321551,"score_spread":0.1834231583038068,"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."}}