{"id":"W4388120021","doi":"10.2139/ssrn.4620446","title":"Atr-Ftir Spectroscopy and Machine/Deep Learning Models for Detecting Substitutions in Coconut Water with Sugars, Sugar Alcohols, and Artificial Sweeteners","year":2023,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Coconut Research and Applications","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph; Canadian Food Inspection Agency","funders":"","keywords":"Sugar; Fourier transform infrared spectroscopy; Artificial Sweetener; Spectroscopy; Chemistry; Food science; Organic chemistry; Chemical engineering; Engineering; 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":[0.0005359378,0.0008474309,0.0004032358,0.000681859,0.0002062852,0.0005395766,0.0005340239,0.0007744266,0.001244728],"category_scores_gemma":[0.001080808,0.0002810843,0.0004029188,0.000581157,0.0002559132,0.001011842,0.0003723325,0.0008714417,0.0003616089],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004590805,"about_ca_system_score_gemma":0.0003303882,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004534086,"about_ca_topic_score_gemma":0.005241327,"domain_scores_codex":[0.9998734,0.00002480971,0.000004628268,0.00004867596,0.00002709008,0.00002135773],"domain_scores_gemma":[0.999709,0.0001258466,0.0000527114,0.00002802666,0.00006302409,0.00002147457],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001364053,0.0007390666,0.01600319,0.0003712332,0.0002083665,0.0002658829,0.000106955,0.2535415,0.4223771,0.004195831,0.002273415,0.2985534],"study_design_scores_gemma":[0.000006971303,0.00003972282,0.0008613601,0.00000301575,0.00001566613,0.00001610358,0.00001204132,0.9683014,0.02986642,0.0007080457,0.0001625709,0.000006699743],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.8030322,0.001836318,0.1895587,0.0005136247,0.000103826,0.00004818645,0.0005296702,0.001467914,0.002909444],"genre_scores_gemma":[0.9218654,0.0005961988,0.07434206,0.00008245267,0.0000303305,0.000027388,0.0003948353,0.0000826446,0.002578709],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.004534086,"threshold_uncertainty_score":0.009015381,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03739590685481754,"score_gpt":0.2875405465016346,"score_spread":0.2501446396468171,"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."}}