{"id":"W1899223088","doi":"10.1002/cyto.a.22707","title":"State‐of‐the‐Art in the Computational Analysis of Cytometry Data","year":2015,"lang":"en","type":"editorial","venue":"Cytometry Part A","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; BC Cancer Agency","funders":"","keywords":"Mass cytometry; Computer science; Cytometry; Lagging; Flow cytometry; Data science; Data mining; Limiting; Statistics; Biology; Mathematics","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":[0.001136249,0.0002358117,0.0005527604,0.0004353193,0.00003890501,0.00002585802,0.001699713,0.0004093679,0.00001456716],"category_scores_gemma":[0.0008173551,0.0001648121,0.0002541107,0.002154483,0.0002123835,0.000005549015,0.0003403008,0.0003797701,0.000004107935],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003108532,"about_ca_system_score_gemma":0.0004877859,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007002272,"about_ca_topic_score_gemma":0.0003131179,"domain_scores_codex":[0.9975935,0.0002158582,0.0005921771,0.0004863587,0.0008932697,0.0002188813],"domain_scores_gemma":[0.9976054,0.000247064,0.0003669904,0.001381483,0.0003486573,0.00005039873],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001139612,0.0002328894,0.01031824,0.00008362999,0.001032791,0.000002827464,0.00009409256,0.001722679,0.002512493,0.000001894774,0.9835492,0.000335301],"study_design_scores_gemma":[0.0007349532,0.0001618214,0.00475534,0.00005161798,0.0008635519,0.000001414887,0.00004624414,0.0009336695,0.00135723,0.00003783153,0.990764,0.0002923092],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"empirical","genre_scores_codex":[0.3916691,0.00548411,0.006453664,0.0001927712,0.5691997,0.001087634,0.02383735,0.0000246919,0.002051014],"genre_scores_gemma":[0.8308005,0.0003317147,0.0004898697,0.0001457229,0.1329802,0.0000207627,0.03415881,0.00007272747,0.0009997275],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4391314,"threshold_uncertainty_score":0.6720844,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03490957247186138,"score_gpt":0.3059825750306068,"score_spread":0.2710730025587454,"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."}}