{"id":"W2751015812","doi":"10.3791/56245","title":"Expedited Radiation Biodosimetry by Automated Dicentric Chromosome Identification (ADCI) and Dose Estimation","year":2017,"lang":"en","type":"article","venue":"Journal of Visualized Experiments","topic":"Carcinogens and Genotoxicity Assessment","field":"Biochemistry, Genetics and Molecular Biology","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"National Institute of Allergy and Infectious Diseases; Health Canada; Canadian Nuclear Laboratories; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Dicentric chromosome; Biodosimetry; Metaphase; Software; Chromosome; Calibration; Computer science; Artificial intelligence; Pattern recognition (psychology); Computer vision; Biology; Mathematics; Ionizing radiation; Genetics; Physics; Statistics; Karyotype","routes":{"ca_aff":true,"ca_fund":true,"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":[0.003457489,0.001114615,0.001012537,0.005619443,0.0006134011,0.001514343,0.00169685,0.001170677,0.01436004],"category_scores_gemma":[0.006230143,0.001189406,0.0007800024,0.002460137,0.0007176552,0.001331975,0.001556732,0.001615785,0.006898759],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001024502,"about_ca_system_score_gemma":0.0008755453,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001144222,"about_ca_topic_score_gemma":0.00262387,"domain_scores_codex":[0.9973182,0.0002866735,0.0002051198,0.0006800026,0.001405782,0.0001042263],"domain_scores_gemma":[0.9953083,0.001226645,0.0005148672,0.0009890768,0.001842877,0.0001182733],"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":[0.0007291831,0.0002363461,0.01270354,0.001154363,0.0001192881,0.0003052045,0.0005923705,0.005137752,0.6248189,0.004697119,0.01619416,0.3333117],"study_design_scores_gemma":[0.00005680907,0.0003485089,0.0229649,0.0001699762,0.00008956622,0.001019017,0.0001795529,0.04233608,0.8741055,0.002124603,0.05643806,0.0001674483],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04819142,0.001045333,0.9194966,0.0002567748,0.0001911948,0.001173539,0.003304214,0.01536897,0.01097198],"genre_scores_gemma":[0.08179376,0.001454246,0.8944742,0.0002729096,0.00005353219,0.002193255,0.003669805,0.002318235,0.01377012],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01436004,"threshold_uncertainty_score":0.04803914,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01582549335308056,"score_gpt":0.4124769873468368,"score_spread":0.3966514939937563,"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."}}