{"id":"W4387776917","doi":"10.3389/frai.2023.1200994","title":"Machine learning algorithms in microbial classification: a comparative analysis","year":2023,"lang":"en","type":"review","venue":"Frontiers in Artificial Intelligence","topic":"Image Processing Techniques and Applications","field":"Engineering","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Machine learning; Artificial intelligence; Computer science; Deep learning; Convolutional neural network; Identification (biology); Context (archaeology); Transfer of learning; Repurposing; Contextual image classification; Image (mathematics); Engineering","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0003691701,0.0003459355,0.001305799,0.001368209,0.00007291965,0.0001032347,0.0004770789,0.0002635301,0.00002142154],"category_scores_gemma":[0.00003379735,0.0003646629,0.0002673255,0.004838235,0.00009985131,0.00008363638,0.00005700656,0.0009267307,0.0001102533],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003217212,"about_ca_system_score_gemma":0.00005849205,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000680726,"about_ca_topic_score_gemma":0.0002075078,"domain_scores_codex":[0.9979659,0.00009760245,0.001010436,0.0004534035,0.0001325082,0.0003401248],"domain_scores_gemma":[0.9993775,0.00008227399,0.0001588805,0.0002942065,0.00003844724,0.00004866937],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000001531725,0.00003148079,0.0000134365,0.001006286,0.0001387904,0.000007473016,0.0002875869,0.005644501,0.000001352837,0.0003778617,0.0003617026,0.992128],"study_design_scores_gemma":[0.000008403335,0.000007724464,0.000002389394,0.001456259,0.0003234481,0.000001517597,0.00016621,0.6610528,0.00004294646,0.001847066,0.3346649,0.0004264032],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[6.544716e-7,0.5582275,0.4405372,0.000009168275,0.000209106,0.0003564807,0.00003169431,0.0003022751,0.0003259833],"genre_scores_gemma":[0.0001667647,0.9594138,0.0394327,0.000003303489,0.00008356314,0.0004163788,0.0002811582,0.00005013393,0.0001521751],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9917016,"threshold_uncertainty_score":0.9998806,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1360913954383835,"score_gpt":0.3769606491794435,"score_spread":0.24086925374106,"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."}}