{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001226286,0.00008386103,0.0001861991,0.00008687606,0.00005380733,0.000005545155,0.0002829894,0.0001326914,0.00004632787],"category_scores_gemma":[0.0002431996,0.00006007858,0.0001065962,0.0000903782,0.0001416522,0.000008250069,0.00003790603,0.000174462,0.000001166],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001667738,"about_ca_system_score_gemma":0.0000485256,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003714176,"about_ca_topic_score_gemma":0.000004661593,"domain_scores_codex":[0.9987542,0.0004474111,0.0004969158,0.00008006734,0.00009867135,0.000122734],"domain_scores_gemma":[0.9991417,0.00006945057,0.0002716618,0.0002441031,0.0002484111,0.0000247017],"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":[0.0001983241,0.0001184463,0.003006905,0.00001016944,0.0001208579,9.062331e-7,0.0001368796,0.00002718798,0.986039,0.0002970993,0.0000345416,0.01000967],"study_design_scores_gemma":[0.0005085039,0.0008079915,0.2270559,0.00001873573,0.0001403292,0.0001756748,0.0001908175,0.0004478975,0.7689016,0.001345773,0.0003028429,0.0001039143],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7154772,0.0011209,0.2823297,0.00009306708,0.0007099488,0.00004629917,0.00000298953,0.000002310163,0.0002175241],"genre_scores_gemma":[0.9538714,0.0003290962,0.04560742,0.00004225786,0.00009958346,9.512792e-7,0.000008297099,0.00001035551,0.00003057731],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2383942,"threshold_uncertainty_score":0.2449933,"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."}}