{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009656477,0.0003874157,0.0003186405,0.001367145,0.0002139653,0.0005709942,0.0002661257,0.0002315248,0.0004423098],"category_scores_gemma":[0.003513848,0.0001768283,0.0002540653,0.0007078828,0.0003301521,0.0003847003,0.0002474187,0.0002956875,0.0002475879],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002772707,"about_ca_system_score_gemma":0.0005546599,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001327418,"about_ca_topic_score_gemma":0.001699283,"domain_scores_codex":[0.999385,0.0002021699,0.00005613498,0.0000897541,0.000230797,0.00003617716],"domain_scores_gemma":[0.9982326,0.0007034228,0.0002933918,0.0001602957,0.0005243003,0.00008590936],"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":[0.0007375804,0.0002417494,0.02970729,0.0002009917,0.00007738102,0.000222829,0.0001364624,0.00968795,0.5937963,0.001669749,0.001213399,0.3623083],"study_design_scores_gemma":[0.0001396796,0.00274833,0.1795085,0.00006157472,0.0001948681,0.003162805,0.0004010033,0.4064597,0.3938932,0.006097666,0.007034583,0.000298035],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.6218407,0.0006710126,0.3735908,0.000341083,0.0000395033,0.0001934104,0.0006203204,0.0009873485,0.001715807],"genre_scores_gemma":[0.7275633,0.0002806985,0.2703648,0.0001068819,0.0000395087,0.0001449841,0.0009404243,0.00003619276,0.0005231941],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.001367145,"threshold_uncertainty_score":0.005106926,"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."}}