{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000835013,0.000145332,0.0002988687,0.0001584526,0.00003958622,0.00003288767,0.0005112852,0.0005447829,0.00008677365],"category_scores_gemma":[0.000644835,0.0001229633,0.0001363505,0.0005265286,0.00009122398,0.000009306868,0.0001817917,0.0003177813,9.245275e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001263385,"about_ca_system_score_gemma":0.00005772277,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005222247,"about_ca_topic_score_gemma":0.000009885563,"domain_scores_codex":[0.9986415,0.0003186762,0.0002631464,0.0004205252,0.0001678359,0.0001883145],"domain_scores_gemma":[0.9985468,0.0001099893,0.00005994067,0.0009664401,0.0002423005,0.00007457631],"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":[0.000008513775,0.0001063266,0.005192302,0.00003385969,0.0003500385,9.047656e-7,0.00000554616,0.00001120011,0.9665443,0.00007867484,0.002042978,0.02562533],"study_design_scores_gemma":[0.0001809881,0.0001633452,0.02923192,0.00001259382,0.0004798515,0.000004478162,0.00001539533,0.08844111,0.8726056,0.0001634126,0.008440016,0.000261329],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04423653,0.001188535,0.9515116,0.0002354306,0.0002792449,0.0002013231,0.0001344046,0.0001234468,0.002089486],"genre_scores_gemma":[0.4518287,0.00003513795,0.5475111,0.0001725835,0.00007293392,0.000007586962,0.0003263433,0.0000106586,0.00003499313],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4075921,"threshold_uncertainty_score":0.50143,"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."}}