{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002875511,0.0000565474,0.0001770397,0.00005467333,0.00002055076,0.00001303727,0.0001147536,0.00007114688,0.0005239317],"category_scores_gemma":[0.0006358021,0.00003009643,0.00004388586,0.00008762949,0.00009010611,0.00003349681,0.000008602427,0.000236055,0.000005629749],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007520577,"about_ca_system_score_gemma":0.0001417004,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002081197,"about_ca_topic_score_gemma":0.00004229567,"domain_scores_codex":[0.9994155,0.00001058716,0.0002182253,0.00005445539,0.0001402135,0.0001610397],"domain_scores_gemma":[0.9993045,0.00008007223,0.00006697234,0.00009517489,0.0002002501,0.0002529964],"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":[0.00005558526,0.000003658439,0.00003896215,0.00001105019,0.00002945333,0.0000161778,0.00002685365,0.000003046928,0.9978593,0.00005757569,0.0005620265,0.001336309],"study_design_scores_gemma":[0.0001952184,0.0001121832,0.002189811,0.00009262712,0.00002273118,0.000100248,0.000004481446,0.00005789308,0.9938747,0.0001686446,0.003142758,0.00003870285],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9977123,0.0003224299,0.00002061911,0.001492885,0.0002598981,0.00004694906,0.000009042385,0.000003009128,0.0001329082],"genre_scores_gemma":[0.9983958,0.000006332741,0.0004474702,0.00008043352,0.001008116,2.489734e-7,0.000002778438,0.000005937269,0.00005290366],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0039846,"threshold_uncertainty_score":0.5736684,"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."}}