{"id":"W2887864821","doi":"10.1186/s13742-016-0147-0-k","title":"Nipype interfaces in CBRAIN","year":2016,"lang":"en","type":"article","venue":"GigaScience","topic":"Characterization and Applications of Magnetic Nanoparticles","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; Montreal Neurological Institute and Hospital","funders":"","keywords":"Computer science; Data science","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":[0.00006894901,0.00003406028,0.00003294298,0.00003860488,0.00001217235,0.00001468297,0.0001280095,0.00001053602,0.0001248294],"category_scores_gemma":[0.00002311165,0.00002409283,0.000005353625,0.0001847864,0.00004712509,0.0001114004,0.00001523417,0.00001487264,0.000169552],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001343287,"about_ca_system_score_gemma":0.000004761728,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001280162,"about_ca_topic_score_gemma":0.000007591082,"domain_scores_codex":[0.9996896,0.000003649183,0.00007708848,0.00007591664,0.00005362134,0.0001001334],"domain_scores_gemma":[0.9998409,0.00002160739,0.000005990258,0.00009843332,0.000006456734,0.00002664434],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[4.672356e-7,0.000005828612,0.001401356,0.000001784931,2.25299e-7,3.104424e-7,0.00006534359,0.00009102377,0.9297704,0.001011834,0.0002067408,0.06744476],"study_design_scores_gemma":[0.0004123045,0.00002506573,0.1033759,0.00005981917,0.000001058514,0.00000419881,0.00006224416,0.007005377,0.8611529,0.001651507,0.0260316,0.0002179783],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9916693,0.0000282614,0.005209482,0.0008757092,0.00005922477,0.00004183219,0.000001654776,0.00008959272,0.002024969],"genre_scores_gemma":[0.9992723,0.00002413952,0.0004072353,0.00004212735,0.000008538917,0.00001086914,1.468541e-7,0.000003176057,0.0002314734],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1019746,"threshold_uncertainty_score":0.2179305,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004547887607408561,"score_gpt":0.1749953566646816,"score_spread":0.170447469057273,"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."}}