{"id":"W2042829929","doi":"10.1002/cyto.a.20531","title":"Automated gating of flow cytometry data via robust model‐based clustering","year":2008,"lang":"en","type":"article","venue":"Cytometry Part A","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":275,"is_retracted":false,"has_abstract":true,"ca_institutions":"Terry Fox Research Institute; University of British Columbia","funders":"Michael Smith Health Research BC","keywords":"Computer science; Cluster analysis; Outlier; Mixture model; Robustness (evolution); Data mining; Cytometry; Expectation–maximization algorithm; Statistical model; Transformation (genetics); Artificial intelligence; Maximum likelihood; Flow cytometry; Statistics; Mathematics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0011106,0.0002970953,0.0005229112,0.0005989323,0.0002342411,0.00007032152,0.002339588,0.0001731945,0.00002018111],"category_scores_gemma":[0.0002181168,0.0002865846,0.0001136952,0.002404705,0.0001141046,0.0008245229,0.001154782,0.0002820815,0.00002168981],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006320428,"about_ca_system_score_gemma":0.0001973647,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001747705,"about_ca_topic_score_gemma":0.000003675747,"domain_scores_codex":[0.9972288,0.0001765536,0.0006191282,0.0008500073,0.0005744555,0.0005510244],"domain_scores_gemma":[0.9966813,0.0002286124,0.0002699477,0.002446914,0.0001524921,0.0002207105],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001442106,0.001349802,0.008868182,0.001155884,0.0004367235,0.0006279516,0.002774549,0.6246839,0.06508062,0.001884365,0.02426958,0.2687242],"study_design_scores_gemma":[0.0004927311,0.00004975034,0.0005493917,0.00007972748,0.00001540104,0.00007660861,0.000003272711,0.9927831,0.005323014,0.0001973946,0.0001140334,0.0003155612],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009002985,0.0001802376,0.9883643,0.00009781982,0.000342367,0.0001899202,0.00004520651,0.0007619906,0.00101516],"genre_scores_gemma":[0.4331763,0.000008377923,0.5664641,0.0001807934,0.00006584422,0.000006426005,0.00002560979,0.0000206797,0.00005186773],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4241733,"threshold_uncertainty_score":0.9999586,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1051308348991267,"score_gpt":0.3155985677931308,"score_spread":0.2104677328940041,"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."}}