{"id":"W2980300981","doi":"10.1109/ccece.2019.8861843","title":"NIR-Spectroscopic Classification of Blood Glucose Level using Machine Learning Approach","year":2019,"lang":"en","type":"article","venue":"","topic":"Spectroscopy Techniques in Biomedical and Chemical Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Support vector machine; Random forest; Principal component analysis; Artificial intelligence; Computer science; Diabetes mellitus; Machine learning; Pattern recognition (psychology); Medicine; Endocrinology","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":[0.0003848879,0.0004829554,0.0005439449,0.001094262,0.0001944168,0.0004734427,0.0003791458,0.0006086616,0.001000392],"category_scores_gemma":[0.0007604788,0.0001297628,0.0005260296,0.001018328,0.0001699331,0.000491503,0.0001758108,0.0003805275,0.000576417],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002745968,"about_ca_system_score_gemma":0.0002794706,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001208424,"about_ca_topic_score_gemma":0.0009697506,"domain_scores_codex":[0.9997322,0.00005054224,0.00001743169,0.00006073786,0.0001056497,0.00003351513],"domain_scores_gemma":[0.9998128,0.00006509194,0.00002969147,0.00001440254,0.00007114061,0.000006892867],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004091734,0.0006943563,0.01284095,0.0004262109,0.0001643147,0.0002808,0.0001187725,0.1757734,0.1370622,0.002673423,0.002743423,0.666813],"study_design_scores_gemma":[0.000006315987,0.0001148549,0.005078553,0.00001272971,0.00002387357,0.0001166406,0.0000318551,0.9719935,0.02094927,0.0008715198,0.0007811844,0.00001972112],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1625497,0.0008849759,0.8305288,0.0002216167,0.0001240974,0.00009639594,0.0003044499,0.00160312,0.003686767],"genre_scores_gemma":[0.7890273,0.0006967126,0.206789,0.00007638315,0.00005959969,0.0001119494,0.0005164361,0.00003225054,0.002690304],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001208424,"threshold_uncertainty_score":0.003346682,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.034174500155858,"score_gpt":0.3224176738708583,"score_spread":0.2882431737150004,"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."}}