{"id":"W4213091682","doi":"10.1016/j.talanta.2022.123327","title":"Using machine learning and an electronic tongue for discriminating saliva samples from oral cavity cancer patients and healthy individuals","year":2022,"lang":"en","type":"article","venue":"Talanta","topic":"Advanced Chemical Sensor Technologies","field":"Engineering","cited_by":51,"is_retracted":false,"has_abstract":false,"ca_institutions":"Dalhousie University","funders":"Instituto Nacional de Ciência e Tecnologia em Eletrônica Orgânica; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Fundação de Amparo à Pesquisa do Estado de São Paulo","keywords":"Support vector machine; Random forest; Artificial intelligence; Tongue; Electronic tongue; Saliva; Machine learning; Cancer; Pattern recognition (psychology); Kernel (algebra); Chemistry; Computer science; Pathology; Mathematics; Internal medicine; Medicine","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":[0.0006371711,0.0003050309,0.0003662453,0.0008880914,0.0002666266,0.0008066749,0.0001651232,0.0006958559,0.0006512055],"category_scores_gemma":[0.001014169,0.0001121238,0.0003570297,0.0004976246,0.0002319977,0.0003216226,0.0003643526,0.0002422555,0.0003477006],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001322928,"about_ca_system_score_gemma":0.0001952578,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005882838,"about_ca_topic_score_gemma":0.0008396699,"domain_scores_codex":[0.9996384,0.0001085319,0.00003987139,0.00007940103,0.00009073487,0.00004307061],"domain_scores_gemma":[0.9996823,0.0001571571,0.00004518261,0.00001881711,0.00007583677,0.0000208254],"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":[0.004525834,0.0005312667,0.3361844,0.000405556,0.0002745289,0.0009239488,0.0005299437,0.002167036,0.3366251,0.0004210282,0.0008871334,0.3165244],"study_design_scores_gemma":[0.0001815546,0.003949475,0.5855999,0.0001325729,0.000979488,0.00612976,0.002415139,0.1523849,0.2408667,0.001171643,0.005995247,0.0001936722],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9894471,0.0007989318,0.007779954,0.0001149834,0.00007585426,0.00003550319,0.0002445726,0.00006909126,0.001433924],"genre_scores_gemma":[0.9901585,0.0003192158,0.008264211,0.00007890361,0.00003007236,0.0000237859,0.0001745885,0.000005493134,0.0009451942],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0008880914,"threshold_uncertainty_score":0.003369749,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02971424213636564,"score_gpt":0.3062039301243701,"score_spread":0.2764896879880044,"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."}}