{"id":"W2949489061","doi":"10.1002/jemt.22642","title":"Automated discrimination of dicentric and monocentric chromosomes by machine learning‐based image processing","year":2016,"lang":"en","type":"article","venue":"Microscopy Research and Technique","topic":"Genomic variations and chromosomal abnormalities","field":"Biochemistry, Genetics and Molecular Biology","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"Response Biomedical (Canada); Canadian Nuclear Laboratories; Health Canada; Cytodiagnostics (Canada); London Health Sciences Centre; Western University","funders":"","keywords":"Dicentric chromosome; Metaphase; Support vector machine; Chromosome; Artificial intelligence; Centromere; Giemsa stain; Pattern recognition (psychology); Computer science; Biology; Karyotype; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003179442,0.00009689364,0.000105271,0.0001166362,0.0001584131,0.00004088296,0.00008794414,0.00009413869,0.00001572914],"category_scores_gemma":[0.0000792654,0.00006996396,0.00001760638,0.0001469746,0.0002474159,0.00001149474,0.0001037625,0.0000829295,8.068334e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001923877,"about_ca_system_score_gemma":0.0000726365,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005992812,"about_ca_topic_score_gemma":0.000005251854,"domain_scores_codex":[0.999175,0.0000909761,0.0001506309,0.0002333867,0.0001107755,0.0002392621],"domain_scores_gemma":[0.9995778,0.0000243769,0.00005978614,0.0001115669,0.000160125,0.00006634956],"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":[0.00004857296,0.00006159321,0.006443198,0.0001183575,0.000006894991,9.447852e-7,0.00002381753,1.239642e-7,0.9907448,0.00002279249,0.0003892528,0.002139712],"study_design_scores_gemma":[0.0004272878,0.0004328622,0.001050844,0.00006660708,0.000004552002,0.000008288336,0.00003352773,0.0001764653,0.9943449,0.00009231335,0.003270694,0.00009163777],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9830872,0.003301107,0.01261369,0.0002836704,0.000007783136,0.0003713616,0.0001140808,0.00005363201,0.0001674946],"genre_scores_gemma":[0.9955821,0.001078229,0.002595301,0.000008816722,0.00001494115,0.00005799928,0.00008741176,0.00001461883,0.0005606008],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0124949,"threshold_uncertainty_score":0.2853048,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01182339336644872,"score_gpt":0.3028798375539496,"score_spread":0.2910564441875009,"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."}}