{"id":"W4321002108","doi":"10.1186/s12859-023-05168-5","title":"petiteFinder: an automated computer vision tool to compute Petite colony frequencies in baker’s yeast","year":2023,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto; Simons Foundation","keywords":"Biology; Yeast; Computer science; Artificial intelligence; Object detection; Object (grammar); Computational biology; Pattern recognition (psychology); Genetics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004017118,0.0002089512,0.0002321967,0.0003019744,0.00007000013,0.0001264824,0.0003483664,0.0001681307,0.00001513793],"category_scores_gemma":[0.00005628189,0.0002050305,0.00009556437,0.0005879481,0.00005656695,0.0000309005,0.0003407909,0.00009855212,0.0002961956],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003649985,"about_ca_system_score_gemma":0.00008162877,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003536674,"about_ca_topic_score_gemma":0.0003662347,"domain_scores_codex":[0.9986199,0.0000508829,0.0004951537,0.0002575103,0.0002256186,0.0003509697],"domain_scores_gemma":[0.9990637,0.00001690143,0.0001081294,0.0005873018,0.000115199,0.0001088207],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003241111,0.0006878583,0.02791055,0.0008640049,0.0002157626,0.0001210287,0.0028641,0.0522712,0.3124873,0.0004804267,0.553948,0.04782556],"study_design_scores_gemma":[0.0004695974,0.0005833579,0.02441674,0.0000732715,0.00001601794,0.00002444475,0.0001733864,0.9237451,0.01278827,0.00002089285,0.037233,0.0004559094],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.9042778,0.00002399743,0.09280435,0.0001346847,0.00008809648,0.0005531074,0.00003446183,0.0007630104,0.001320462],"genre_scores_gemma":[0.2884971,0.0001642922,0.7048303,0.002532793,0.0003573191,0.00008167093,0.002299759,0.00008640497,0.001150235],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8714739,"threshold_uncertainty_score":0.8360901,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01227646958655772,"score_gpt":0.2896800436631122,"score_spread":0.2774035740765545,"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."}}