{"id":"W2112493011","doi":"10.1038/nmeth.2365","title":"Critical assessment of automated flow cytometry data analysis techniques","year":2013,"lang":"en","type":"article","venue":"Nature Methods","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":622,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; BC Cancer Agency","funders":"National Institute of Biomedical Imaging and Bioengineering; National Institute of Neurological Disorders and Stroke; National Institute of Allergy and Infectious Diseases; National Institute of General Medical Sciences; National Cancer Institute","keywords":"Computer science; Identification (biology); Population; Gating; Data mining; Sample (material); Artificial intelligence; Biology; Medicine","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":[0.08183734,0.001729701,0.0009435181,0.005467407,0.002025107,0.005332923,0.003221767,0.001878262,0.002447088],"category_scores_gemma":[0.2441724,0.0008204459,0.001256274,0.002628121,0.002433818,0.003486299,0.003378377,0.002179964,0.00116316],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004250958,"about_ca_system_score_gemma":0.005609906,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002230405,"about_ca_topic_score_gemma":0.00163003,"domain_scores_codex":[0.9009675,0.03412403,0.00960673,0.006321032,0.04755715,0.001423597],"domain_scores_gemma":[0.6516889,0.1999376,0.01895671,0.02190891,0.1054869,0.002021072],"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":[0.002210076,0.0004337612,0.02769481,0.003512148,0.0007035287,0.0006729999,0.004172871,0.01932578,0.07545361,0.02301635,0.02755735,0.8152466],"study_design_scores_gemma":[0.0004778318,0.003966407,0.0622089,0.003831649,0.0009343988,0.00327971,0.003344811,0.2673595,0.3743482,0.05200359,0.2271211,0.001123858],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08614055,0.008401112,0.8785033,0.004947489,0.002041966,0.003856656,0.001065365,0.004692554,0.01035087],"genre_scores_gemma":[0.3232726,0.002525414,0.66327,0.002412971,0.0005959886,0.002484287,0.001450878,0.001341757,0.002646061],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9181626,"threshold_uncertainty_score":0.4328024,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02645300500086839,"score_gpt":0.4456054761658771,"score_spread":0.4191524711650087,"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."}}