{"id":"W4291964186","doi":"10.1128/spectrum.01472-22","title":"A Deep Learning Approach to Capture the Essence of Candida albicans Morphologies","year":2022,"lang":"en","type":"article","venue":"Microbiology Spectrum","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University; Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Government of Canada","keywords":"Candida albicans; Deep learning; Corpus albicans; Biology; Yeast; Artificial intelligence; Computer science; Computational biology; Microbiology; 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":[0.000269471,0.000138953,0.0001938013,0.00006657292,0.0002328444,0.000008089446,0.0005535119,0.00008134856,0.0001129624],"category_scores_gemma":[0.00005517463,0.0001082377,0.0001129787,0.0002259623,0.0001984672,0.000001371269,0.0006194281,0.000275613,0.000003721287],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002888917,"about_ca_system_score_gemma":0.00003651993,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003429326,"about_ca_topic_score_gemma":0.0001892806,"domain_scores_codex":[0.9989532,0.0001594234,0.0001798708,0.0003882154,0.00004547484,0.0002738167],"domain_scores_gemma":[0.9993736,0.00001693339,0.0001069128,0.0004485169,0.00002686479,0.00002718263],"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":[0.00003669416,0.00005006481,0.001527722,0.000003701225,0.00006519261,0.000003390322,0.000178489,0.002027967,0.9900651,0.0001576054,0.00578163,0.0001024438],"study_design_scores_gemma":[0.0002235436,0.0005479668,0.0007460426,0.000002147435,0.00006004539,0.0003556788,0.001958349,0.0001419582,0.8519189,0.0001413425,0.1435916,0.0003124115],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9684468,0.001246962,0.02258483,0.001225214,0.00005159474,0.0003775687,0.00002254704,0.00005198372,0.005992533],"genre_scores_gemma":[0.9963248,0.00003688588,0.0009309693,0.0007267101,0.0000375448,0.00008119609,0.000214353,0.00001599039,0.001631593],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1381462,"threshold_uncertainty_score":0.4413806,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004338778385253716,"score_gpt":0.2135109643741734,"score_spread":0.2091721859889197,"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."}}