{"id":"W4311935231","doi":"10.1128/aem.01828-22","title":"Accelerating the Detection of Bacteria in Food Using Artificial Intelligence and Optical Imaging","year":2022,"lang":"en","type":"article","venue":"Applied and Environmental Microbiology","topic":"Spectroscopy Techniques in Biomedical and Chemical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":58,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; U.S. Department of Agriculture; National Institute of Food and Agriculture; National Science Foundation","keywords":"Bacteria; Artificial intelligence; Biology; Computer science; Biological system; Genetics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004185773,0.0004982675,0.0003489683,0.0007125387,0.0002440605,0.0006709817,0.0003926007,0.0006136835,0.0003562497],"category_scores_gemma":[0.0006160488,0.0002294284,0.0003422171,0.0004780342,0.0004540352,0.0007723729,0.0005141226,0.0005563494,0.0001898499],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002723654,"about_ca_system_score_gemma":0.0002838336,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005485833,"about_ca_topic_score_gemma":0.000894394,"domain_scores_codex":[0.999624,0.00007819768,0.00001952425,0.00007623645,0.0001606196,0.00004140803],"domain_scores_gemma":[0.9997171,0.0001180079,0.00006762185,0.00001639362,0.00006508272,0.00001587488],"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":[0.00006783855,0.0001190742,0.001678855,0.0001637238,0.00001945332,0.00006086858,0.00006224777,0.003011935,0.9273636,0.001003949,0.0002136005,0.06623475],"study_design_scores_gemma":[0.0000137105,0.0006250399,0.00602608,0.00003114979,0.00004477854,0.0003078913,0.0001187861,0.1139243,0.8709164,0.001518435,0.006415683,0.00005781101],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5326724,0.00521998,0.4527369,0.0008269107,0.0002339268,0.0001537373,0.00009689842,0.0009905214,0.007068829],"genre_scores_gemma":[0.6520601,0.002270476,0.3428176,0.0003451911,0.00006837617,0.0001064063,0.00009481925,0.00004280957,0.002194184],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0007125387,"threshold_uncertainty_score":0.002213657,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01444393404407011,"score_gpt":0.2506051631890391,"score_spread":0.236161229144969,"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."}}