{"id":"W4390120279","doi":"10.1093/mmy/myad134","title":"Machine learning to identify clinically relevant <i>Candida</i> yeast species","year":2023,"lang":"en","type":"article","venue":"Medical Mycology","topic":"Antifungal resistance and susceptibility","field":"Medicine","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Canada Foundation for Innovation; Government of Alberta","keywords":"Candida albicans; Budding yeast; Candida glabrata; Yeast; Biology; Corpus albicans; Candida auris; Convolutional neural network; Artificial intelligence; Microbiology; Candida infections; Antifungal; Saccharomyces cerevisiae; Computer science; Biochemistry","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":["metaresearch","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.001737124,0.0001532235,0.0005463444,0.0001482232,0.0001215771,0.00001255488,0.00020623,0.0002935681,0.005049928],"category_scores_gemma":[0.01128895,0.0001201742,0.0001547907,0.000568149,0.0002744265,0.00002731183,0.00021761,0.0008143298,0.004312271],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006469442,"about_ca_system_score_gemma":0.000262982,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004547817,"about_ca_topic_score_gemma":0.0001708756,"domain_scores_codex":[0.9974567,0.0001904797,0.0005957807,0.0004694191,0.000744889,0.0005427387],"domain_scores_gemma":[0.9982034,0.0006455869,0.0000676968,0.0003211963,0.00009926807,0.0006628643],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001531866,0.0003360597,0.8630625,0.0001896855,0.0001138007,0.002901098,0.0005816527,0.000005906003,0.01907534,0.001003579,0.08781153,0.02338696],"study_design_scores_gemma":[0.001062119,0.0009078528,0.7080138,0.000102606,0.00004502463,0.00005750687,0.0002617376,0.0003253028,0.0001248928,0.0002665138,0.2886993,0.0001333138],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9636784,0.0001508118,0.0004694248,0.02080838,0.0007716865,0.0003394314,0.00001301384,0.0003486372,0.01342026],"genre_scores_gemma":[0.9739972,0.0005772267,0.0001978553,0.005441222,0.0005107522,0.00002403665,0.00006061735,0.00002526274,0.0191658],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2008878,"threshold_uncertainty_score":0.9970394,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03059813290932772,"score_gpt":0.3647091864544804,"score_spread":0.3341110535451527,"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."}}