{"id":"W2592730573","doi":"10.1002/cyto.b.21519","title":"Visualization of Cell Composition and Maturation in the Bone Marrow Using 10‐Color Flow Cytometry and Radar Plots","year":2017,"lang":"en","type":"article","venue":"Cytometry Part B Clinical Cytometry","topic":"Acute Myeloid Leukemia Research","field":"Medicine","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; Toronto General Hospital","funders":"","keywords":"Flow cytometry; Bone marrow; Visualization; Haematopoiesis; Cell; Cytometry; Computational biology; Biology; Computer science; Stem cell; Immunology; Data mining; Cell biology; Genetics","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":[],"consensus_categories":[],"category_scores_codex":[0.001766577,0.0006248796,0.0002964038,0.001925932,0.0001941578,0.001071558,0.000667832,0.0005918919,0.003191043],"category_scores_gemma":[0.001352286,0.0002824355,0.0003833857,0.0007343489,0.0005316435,0.0006434305,0.0006285557,0.0005836905,0.001256273],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005075347,"about_ca_system_score_gemma":0.000389591,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004992323,"about_ca_topic_score_gemma":0.0003945128,"domain_scores_codex":[0.9992892,0.0002514926,0.00006763036,0.0001645035,0.0001584456,0.00006870705],"domain_scores_gemma":[0.9990913,0.0003651844,0.0002260997,0.0001068329,0.0001499659,0.00006074177],"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":[0.0004976484,0.00009329311,0.01139296,0.0003174991,0.00005366782,0.0002236558,0.0002150975,0.005162301,0.9326324,0.003010433,0.0008232472,0.04557777],"study_design_scores_gemma":[0.0000865118,0.000685791,0.04725604,0.000112214,0.0001153221,0.002534804,0.0002535179,0.1376515,0.7931468,0.002935077,0.01510528,0.0001171121],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.4956636,0.001787409,0.4950138,0.000304779,0.00005283864,0.0002197558,0.0008623697,0.00323507,0.002860373],"genre_scores_gemma":[0.5630447,0.001299205,0.4319993,0.0001334991,0.0000364887,0.0003439744,0.001432966,0.0002931627,0.00141666],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.003191043,"threshold_uncertainty_score":0.01067513,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08758636724954841,"score_gpt":0.4193764445140727,"score_spread":0.3317900772645243,"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."}}