{"id":"W2790849353","doi":"10.1038/s41374-018-0037-4","title":"Correction to: Use of multicolor fluorescence in situ hybridization to detect deletions in clinical tissue sections","year":2018,"lang":"en","type":"erratum","venue":"Laboratory Investigation","topic":"Tissue Engineering and Regenerative Medicine","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa; Children's Hospital of Eastern Ontario; Queen's University; University Health Network; University of Alberta","funders":"","keywords":"In situ; Fluorescence in situ hybridization; In situ hybridization; Fluorescence; Pathology; Computational biology; Biology; Artificial intelligence; Natural language processing; Computer science; Medicine; Chemistry; Genetics; Physics; Optics; Chromosome; Gene; Gene expression","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":[0.003302816,0.001770438,0.001512886,0.003558866,0.002471572,0.002783645,0.003337668,0.006245831,0.05581438],"category_scores_gemma":[0.04562453,0.001330437,0.001322611,0.001610666,0.002327093,0.001636135,0.002025515,0.008807787,0.03194383],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002932966,"about_ca_system_score_gemma":0.003843309,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008224686,"about_ca_topic_score_gemma":0.01024706,"domain_scores_codex":[0.995595,0.0006185911,0.0008775894,0.000577871,0.001926685,0.000404227],"domain_scores_gemma":[0.9698529,0.007027845,0.001555817,0.002010612,0.01810892,0.001443777],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005104861,0.00001093781,0.0001132494,0.0001510521,0.00001118022,0.0007310685,0.00003932813,0.00005316738,0.0001934117,0.0006015376,0.9886431,0.009400862],"study_design_scores_gemma":[0.00003950971,0.00002994302,0.0007916811,0.0002217132,0.00003525042,0.001747133,0.00008362324,0.0002908629,0.0008271835,0.0008643845,0.995034,0.00003474875],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"other","genre_scores_codex":[0.0003054277,0.0005677599,0.001496878,0.02784091,0.9663575,0.00002626547,0.0006615073,0.0005006589,0.002243072],"genre_scores_gemma":[0.02512814,0.008277066,0.02367428,0.09171396,0.4179919,0.000529759,0.004545279,0.004625616,0.4235139],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.05581438,"threshold_uncertainty_score":0.1867177,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03575858850087887,"score_gpt":0.322905968771686,"score_spread":0.2871473802708072,"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."}}