{"id":"W4206448656","doi":"10.3390/pr10010128","title":"Special Issue on “Phenolic Compounds: Extraction, Optimization, Identification and Applications in Food Industry”","year":2022,"lang":"en","type":"article","venue":"Processes","topic":"Phytochemicals and Antioxidant Activities","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Identification (biology); Extraction (chemistry); Biochemical engineering; Food industry; Computer science; Chemistry; Engineering; Food science; Chromatography; Biology; Botany","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":[0.0000638042,0.00007597219,0.0001150637,0.0001184579,0.0001756871,0.00003033367,0.00005101178,0.00005985356,0.0002346775],"category_scores_gemma":[0.00007010481,0.00007820807,0.00001143122,0.0004334778,0.00003903133,0.0001222451,0.00002638385,0.0003293668,0.000003185026],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000488425,"about_ca_system_score_gemma":0.0000812625,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005566632,"about_ca_topic_score_gemma":0.000005526418,"domain_scores_codex":[0.999373,0.00001350696,0.0001656659,0.0001926396,0.000160091,0.00009506353],"domain_scores_gemma":[0.9996579,0.00005449775,0.00008052616,0.00009786548,0.00007275571,0.00003648708],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002984663,0.03011159,0.3753386,0.01876695,0.0007350535,0.00006092756,0.02683493,0.06207128,0.1502765,0.01554503,0.2019254,0.1153491],"study_design_scores_gemma":[0.00237972,0.0006175654,0.02974152,0.0001796703,0.00009293907,0.0001554654,0.008671935,0.0008240424,0.100964,0.004078608,0.8516974,0.0005971124],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9620678,0.00148257,0.001819702,0.004766698,0.0002742002,0.00147915,0.00009674805,0.000180189,0.02783291],"genre_scores_gemma":[0.9968619,0.0001543312,0.0001273342,0.0001775456,0.001609701,0.000344264,0.00008253409,0.00001217475,0.0006301924],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.649772,"threshold_uncertainty_score":0.3189232,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01860472688656498,"score_gpt":0.2814561175999711,"score_spread":0.2628513907134061,"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."}}