{"id":"W2171621990","doi":"10.1128/aem.69.8.4566-4574.2003","title":"Rapid Identification of<i>Candida</i>Species by Using Nuclear Magnetic Resonance Spectroscopy and a Statistical Classification Strategy","year":2003,"lang":"en","type":"article","venue":"Applied and Environmental Microbiology","topic":"Fermentation and Sensory Analysis","field":"Agricultural and Biological Sciences","cited_by":72,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Institute for Biodiagnostics","funders":"National Medical Research Council; National Health and Medical Research Council","keywords":"Nuclear magnetic resonance spectroscopy; Candida tropicalis; Biology; Candida glabrata; Yeast; Proton NMR; Nuclear magnetic resonance; Candida parapsilosis; Two-dimensional nuclear magnetic resonance spectroscopy; Chemotaxonomy; NMR spectra database; Candida albicans; Computational biology; Microbiology; Biochemistry; Spectral line; Botany; Taxonomy (biology); Physics","routes":{"ca_aff":true,"ca_fund":false,"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":[0.00007194266,0.00008760108,0.0001255625,0.000008673258,0.0001179361,0.00001782874,0.00003765045,0.00005990837,0.0005506355],"category_scores_gemma":[0.000002574777,0.00004734865,0.00001553769,0.0000477666,0.0003162482,0.00002684825,0.00001378396,0.00004535569,0.000007207683],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001600787,"about_ca_system_score_gemma":0.00000131215,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001308,"about_ca_topic_score_gemma":0.000004617159,"domain_scores_codex":[0.9993784,0.00004516675,0.0001923189,0.0002339186,0.00003147273,0.0001187563],"domain_scores_gemma":[0.9998152,0.00004065813,0.00007187458,0.00003345803,0.00000200991,0.00003682331],"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":[0.00001642972,0.00004252033,0.001328066,0.000002329903,0.000004607332,1.891476e-7,0.00003613696,5.056132e-7,0.9883024,0.002292122,0.00007350162,0.007901216],"study_design_scores_gemma":[0.0009872278,0.0006443566,0.3973207,0.000007868068,0.0001245505,0.00006986708,0.007724695,0.0004616205,0.5330619,0.001196536,0.05780409,0.0005965026],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9985736,0.0007599917,0.00001816597,0.00003878273,0.00001121238,0.00009650066,0.000213897,0.000006407128,0.0002814304],"genre_scores_gemma":[0.9985916,0.0008170909,0.0001976195,0.00008452014,0.000007896815,0.000002977638,0.0002229938,0.000001090195,0.00007419572],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4552404,"threshold_uncertainty_score":0.6029072,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01200866762222876,"score_gpt":0.1885760000815364,"score_spread":0.1765673324593076,"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."}}