{"id":"W130983363","doi":"10.4049/jimmunol.186.supp.65.2","title":"FlowCAP: critical assessment of flow cytometry population identification methods (65.2)","year":2011,"lang":"en","type":"article","venue":"The Journal of Immunology","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"BC Cancer Agency","funders":"","keywords":"Computer science; Cluster analysis; Population; Identification (biology); Data set; Data mining; Gating; Set (abstract data type); Artificial intelligence; Pattern recognition (psychology); Biology","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.06208035,0.004075331,0.002163039,0.01033735,0.002770796,0.004817348,0.005441179,0.002024277,0.02838906],"category_scores_gemma":[0.1019644,0.001826279,0.001763903,0.004837906,0.001372312,0.002812292,0.004072027,0.003681442,0.01147449],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002760819,"about_ca_system_score_gemma":0.007156535,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001699853,"about_ca_topic_score_gemma":0.001284387,"domain_scores_codex":[0.9598789,0.01282116,0.004145185,0.002741596,0.01937766,0.001035531],"domain_scores_gemma":[0.8752983,0.0577863,0.005226186,0.007559652,0.05234664,0.001782896],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.003044239,0.001002536,0.01079623,0.005512462,0.0005325827,0.0004689773,0.0009100913,0.01223393,0.04915244,0.01679913,0.4443684,0.455179],"study_design_scores_gemma":[0.00101464,0.001941434,0.02822925,0.002445548,0.0005849965,0.001755579,0.0006169474,0.1916765,0.2492187,0.0248915,0.4968646,0.0007603767],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0278172,0.005265485,0.811879,0.004554463,0.003117613,0.01226348,0.03149155,0.07937194,0.02423928],"genre_scores_gemma":[0.07452851,0.002296387,0.8398268,0.001928073,0.0008025724,0.02808252,0.03196585,0.01121141,0.009357977],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9379197,"threshold_uncertainty_score":0.3283162,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03578794012115271,"score_gpt":0.3488991644444571,"score_spread":0.3131112243233044,"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."}}