{"id":"W2181067433","doi":"10.1002/cyto.a.22732","title":"A benchmark for evaluation of algorithms for identification of cellular correlates of clinical outcomes","year":2015,"lang":"en","type":"article","venue":"Cytometry Part A","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":75,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; BC Cancer Agency","funders":"National Institute of Allergy and Infectious Diseases; National Institute of Biomedical Imaging and Bioengineering; Vlaamse regering; U.S. Public Health Service; National Cancer Institute; Ovarian Cancer Research Fund; National Institutes of Health; Natural Sciences and Engineering Research Council of Canada; Lupus Research Alliance; International Society for Advancement of Cytometry; Canada's Michael Smith Genome Sciences Centre; National Heart, Lung, and Blood Institute; U.S. Department of Defense","keywords":"Flow cytometry; Peripheral blood mononuclear cell; Algorithm; Cytometry; Population; Benchmark (surveying); Identification (biology); Computer science; Medicine; Immunology; Biology; In vitro","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":[0.002140068,0.00007936414,0.0002814684,0.00006177796,0.00001473833,0.000003319137,0.0001211689,0.0001561023,0.000004034993],"category_scores_gemma":[0.001278598,0.00007329395,0.0002289885,0.0001025115,0.00008254321,0.000003841731,0.00001648699,0.00002827311,4.428461e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000006860462,"about_ca_system_score_gemma":0.0001273365,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005880453,"about_ca_topic_score_gemma":0.000002717129,"domain_scores_codex":[0.9987371,0.00006223257,0.0007127339,0.0001864521,0.0002078364,0.00009364253],"domain_scores_gemma":[0.9983851,0.00009553341,0.0003877999,0.0002385232,0.0008471272,0.00004592527],"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":[0.0005469546,0.0005655069,0.3083565,0.0001987863,0.000282656,5.03131e-8,0.0001277301,0.00031185,0.6779616,0.00009227948,0.001256699,0.01029942],"study_design_scores_gemma":[0.004345501,0.0009423713,0.02413018,0.00002354304,0.0003315673,4.195462e-7,0.000137514,0.01562172,0.9518248,0.0005834892,0.001919193,0.0001396326],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9360754,0.000487046,0.06204302,0.00001278823,0.0007107744,0.0005016895,0.0001147198,0.000002540703,0.00005197207],"genre_scores_gemma":[0.9972093,0.00002038096,0.002107673,0.000008962293,0.000106428,0.00004760599,0.0003676151,0.00001124438,0.0001207951],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2842263,"threshold_uncertainty_score":0.2988841,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1488719244516753,"score_gpt":0.4015902594677722,"score_spread":0.2527183350160969,"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."}}