{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00004205676,0.0001205653,0.0001636175,0.00003731293,0.0002117441,0.00001887486,0.00007694009,0.00003824577,0.00001513805],"category_scores_gemma":[0.00004898642,0.0001258081,0.0000127625,0.00006386588,0.00003185627,0.00009583576,0.0001343534,0.0002769319,5.582399e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001619993,"about_ca_system_score_gemma":0.000004328742,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001046243,"about_ca_topic_score_gemma":0.000372455,"domain_scores_codex":[0.9992679,0.00002079973,0.0001246005,0.0001990746,0.00009064084,0.0002969223],"domain_scores_gemma":[0.9997501,0.00008908135,0.00004392309,0.00006661657,0.000008877019,0.00004144733],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00006643654,0.00006716107,0.5856853,0.0001639663,0.00008097113,0.000002071642,0.001363904,0.008141192,0.3623167,0.0002336159,0.00002996687,0.04184871],"study_design_scores_gemma":[0.007354244,0.002053351,0.08581675,0.0001573113,0.0003927077,0.00003664379,0.005746305,0.4183273,0.4451229,0.01982225,0.012153,0.00301725],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9976818,0.001158985,0.0001810915,0.00003054616,0.00003765391,0.0001653032,0.0004883461,0.0002511313,0.000005134571],"genre_scores_gemma":[0.9981799,0.0001393889,0.001357921,0.00002243566,0.00002274225,0.00003649471,0.0002051444,0.00003288926,0.000003151353],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4998685,"threshold_uncertainty_score":0.5130306,"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."}}