{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003588379,0.0007874933,0.0004128534,0.000678057,0.000333534,0.0008203146,0.001138357,0.0009946233,0.001422916],"category_scores_gemma":[0.0006802059,0.0003837525,0.0006585327,0.000631017,0.0004260872,0.0007136209,0.0009032622,0.001828965,0.000500441],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001104831,"about_ca_system_score_gemma":0.001061953,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00987904,"about_ca_topic_score_gemma":0.01598967,"domain_scores_codex":[0.9998438,0.00002006323,0.00000766762,0.0000532181,0.00003638148,0.00003882958],"domain_scores_gemma":[0.9997804,0.00005919085,0.0000261344,0.00003179445,0.00007863089,0.00002396152],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001793905,0.0003557425,0.004965735,0.0001647606,0.0001281876,0.0002296803,0.0001534819,0.4880539,0.06954829,0.01665708,0.01382668,0.4057371],"study_design_scores_gemma":[0.000003379394,0.00001757687,0.0003829107,0.000006111757,0.000006355924,0.00001979459,0.0000107383,0.9908888,0.004035872,0.003389657,0.001233155,0.000005643671],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06029911,0.0005811695,0.9322652,0.0006258015,0.0001037934,0.0000777364,0.0008433586,0.002236566,0.002967197],"genre_scores_gemma":[0.5276573,0.0006984235,0.4543694,0.0007446153,0.00009811067,0.0002509047,0.003452871,0.0002102663,0.0125181],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00987904,"threshold_uncertainty_score":0.01964313,"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."}}