{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008970047,0.0003923517,0.0005131432,0.001596562,0.0001590157,0.0006265778,0.000450339,0.0005098364,0.001091702],"category_scores_gemma":[0.00247687,0.0002223949,0.0003295948,0.0005799342,0.000269711,0.0004867669,0.0002894378,0.0003500004,0.0005612613],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002992564,"about_ca_system_score_gemma":0.0001896384,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005729366,"about_ca_topic_score_gemma":0.0008723069,"domain_scores_codex":[0.9994718,0.0001052825,0.00004343008,0.000170307,0.0001591243,0.00004991316],"domain_scores_gemma":[0.9987251,0.0006751092,0.0001833857,0.0001454885,0.0002410121,0.00002997447],"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":[0.0005505935,0.0001163179,0.01637793,0.0002147066,0.0000601737,0.0001308081,0.00009744845,0.01175583,0.5505487,0.0004883333,0.0006713755,0.4189878],"study_design_scores_gemma":[0.00004701959,0.0005115378,0.1397155,0.0000395948,0.00009172486,0.0007444228,0.00008804435,0.3702938,0.4814974,0.002112643,0.004767831,0.00009040551],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6027858,0.001688204,0.3903195,0.000149323,0.00006314291,0.000122968,0.0004338388,0.002321478,0.002115717],"genre_scores_gemma":[0.7621078,0.0004715085,0.2352016,0.00009628395,0.00003401466,0.0001228434,0.000638066,0.00007686199,0.001251018],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001596562,"threshold_uncertainty_score":0.004743874,"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."}}