{"id":"W3083554083","doi":"10.21105/joss.02343","title":"qMRLab: Quantitative MRI analysis, under one umbrella","year":2020,"lang":"en","type":"article","venue":"The Journal of Open Source Software","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":84,"is_retracted":false,"has_abstract":true,"ca_institutions":"Centre Intégré de Santé et Services Sociaux de Chaudière-Appalache; University of Calgary; Centre intégré de santé et de services sociaux de Chaudière-Appalaches; McGill University; Université de Montréal; Polytechnique Montréal; Montreal Neurological Institute and Hospital; Montreal Heart Institute","funders":"Natural Sciences and Engineering Research Council of Canada; Fondation Brain Canada; Institut de Cardiologie de Montréal; Fondation Institut de Cardiologie de Montréal; Canada First Research Excellence Fund; Réseau en Bio-Imagerie du Quebec","keywords":"Medicine; Computer science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003492547,0.0001082065,0.0004002877,0.00007161371,0.0001354683,0.00004745911,0.0005993719,0.00003002762,0.0001410839],"category_scores_gemma":[0.0001960426,0.00007108165,0.0001663647,0.0007551987,0.0000947284,0.0001286697,0.0002153666,0.000418413,0.00002369047],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002927365,"about_ca_system_score_gemma":0.00007497211,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001918693,"about_ca_topic_score_gemma":0.000001660616,"domain_scores_codex":[0.9990318,0.00008400197,0.0003545175,0.000125063,0.000271642,0.0001329661],"domain_scores_gemma":[0.9986631,0.0002478071,0.0003892399,0.000297662,0.0002382682,0.0001639636],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.01632621,0.004628627,0.1626684,0.0007816612,0.02475207,0.0005775633,0.04888071,0.1645821,0.09604969,0.03045212,0.3755834,0.07471741],"study_design_scores_gemma":[0.01325814,0.00894559,0.1501086,0.001435643,0.03441633,0.002061597,0.02068757,0.01176324,0.03209602,0.04030069,0.6827464,0.002180254],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03453426,0.0005060585,0.9234606,0.04070472,0.00001216939,0.000334747,0.000008613425,0.00006297596,0.0003758259],"genre_scores_gemma":[0.8087655,0.0004644385,0.1792472,0.0106214,0.0001432941,0.000005097305,0.000007649515,0.0000467333,0.0006986978],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7742313,"threshold_uncertainty_score":0.2898626,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1990079178057547,"score_gpt":0.4208280736313635,"score_spread":0.2218201558256089,"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."}}