{"id":"W2809120357","doi":"10.1002/cjce.23260","title":"Extraction of wax‐like materials from cereals","year":2018,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Natural Products and Biological Research","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Wax; Extraction (chemistry); Nitrogen; Chromatography; Hexane; Liquid nitrogen; Solvent; Sorghum; Chemistry; Yield (engineering); Materials science; Agronomy; Organic chemistry; Biology; Composite material","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.0001669722,0.0004713323,0.0002435004,0.0003630298,0.0001820935,0.0001829288,0.0001132548,0.0001341681,0.001138979],"category_scores_gemma":[0.0001836315,0.0001216981,0.0003266201,0.0002860269,0.000153203,0.0002819255,0.000200688,0.0002346494,0.0003836124],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001322789,"about_ca_system_score_gemma":0.0002979349,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006816147,"about_ca_topic_score_gemma":0.001778614,"domain_scores_codex":[0.9999166,0.00001594874,0.000007719047,0.00002375474,0.00002263677,0.00001336258],"domain_scores_gemma":[0.9999455,0.00001314122,0.00001121237,0.00000734676,0.00001557151,0.000007152024],"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":[0.00004426387,0.000004465005,0.0002449042,0.0001043069,0.000006818619,0.00006173762,0.00001677894,0.00003091287,0.9965712,0.0000386819,0.00001811729,0.0028579],"study_design_scores_gemma":[0.000008058655,0.0001963617,0.005611533,0.00002532917,0.00003761341,0.0002649312,0.00008044269,0.0003316719,0.9876681,0.00007634515,0.005690766,0.000008944608],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.969479,0.004564434,0.02165185,0.00007036762,0.00009417036,0.0001146767,0.0006845583,0.00007146307,0.003269533],"genre_scores_gemma":[0.9583398,0.003803347,0.03180528,0.00007553285,0.00001685207,0.00006122281,0.001056724,0.00004958755,0.004791589],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001138979,"threshold_uncertainty_score":0.003810287,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02595765608415991,"score_gpt":0.2781302519687096,"score_spread":0.2521725958845497,"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."}}