{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005179634,0.001354518,0.0005443743,0.001445623,0.0002803363,0.001005266,0.002273725,0.001121921,0.005371116],"category_scores_gemma":[0.002427368,0.0005193316,0.0007690855,0.0006343141,0.0003005398,0.001048879,0.0009868668,0.001135629,0.001903097],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001049809,"about_ca_system_score_gemma":0.0006935361,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006794262,"about_ca_topic_score_gemma":0.008579363,"domain_scores_codex":[0.9996306,0.00003393943,0.00002466597,0.0001308961,0.000137614,0.00004221699],"domain_scores_gemma":[0.9993448,0.0002781681,0.00009310296,0.00007252139,0.0001589736,0.00005249138],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001327375,0.0005440995,0.01232729,0.0007845063,0.0003011691,0.0006621823,0.0002367064,0.1992111,0.09139631,0.004898855,0.07462277,0.6136876],"study_design_scores_gemma":[0.00004606983,0.00008585933,0.001936581,0.00002036539,0.00001475627,0.0001453441,0.00003770887,0.9565049,0.03241963,0.003059076,0.005695342,0.00003440151],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.125254,0.0007667864,0.7234163,0.0004001579,0.0001374423,0.0002027704,0.00591641,0.140532,0.003374062],"genre_scores_gemma":[0.3665787,0.0002796387,0.614078,0.0003686693,0.00003546152,0.0003585475,0.01148122,0.002988467,0.003831264],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006794262,"threshold_uncertainty_score":0.01796818,"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."}}