{"id":"W4323637100","doi":"10.1016/j.fuel.2023.128066","title":"Improving yields, compatibility and tailoring the properties of hydrothermal liquefaction bio-crude using yellow grease","year":2023,"lang":"en","type":"article","venue":"Fuel","topic":"Thermochemical Biomass Conversion Processes","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"National Research Council Canada","funders":"National Research Council Canada","keywords":"Hydrothermal liquefaction; Grease; Compatibility (geochemistry); Raw material; Asphaltene; Petroleum; Diesel fuel; Pulp and paper industry; Crude oil; Biodiesel; Biofuel; Chemistry; Materials science; Waste management; Environmental science; Petroleum engineering; Organic chemistry; Composite material; Geology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002069972,0.0003183903,0.0001574711,0.0001686587,0.0001270484,0.0003704216,0.0001998636,0.0002303461,0.0007165096],"category_scores_gemma":[0.0002740431,0.0001441556,0.0001851887,0.0003147889,0.0001228639,0.0004397691,0.0002314327,0.0004417216,0.0003213559],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003218192,"about_ca_system_score_gemma":0.0002757869,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001355571,"about_ca_topic_score_gemma":0.003866446,"domain_scores_codex":[0.999873,0.00001096084,0.00001228402,0.00002461604,0.00004846717,0.00003059641],"domain_scores_gemma":[0.9999073,0.00001490533,0.00002629265,0.00001126548,0.00002948608,0.00001081353],"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":[0.00004490783,0.00001786653,0.0002236817,0.00002542447,0.00000307135,0.00001694654,0.00001526182,0.0001825779,0.9971757,0.0000805188,0.00002045374,0.002193598],"study_design_scores_gemma":[0.000001347574,0.00003374173,0.0003905994,0.000001208598,0.000002791842,0.000008983017,0.000006883795,0.0004668543,0.9987399,0.000007468243,0.0003385211,0.000001763078],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9942211,0.0005286856,0.003855651,0.00004851257,0.00001117231,0.00001247748,0.0001195401,0.00004284426,0.001160093],"genre_scores_gemma":[0.9957497,0.000534763,0.001777265,0.00001132107,0.000003110309,0.00000753624,0.0001427322,0.00002468129,0.001748888],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001355571,"threshold_uncertainty_score":0.002695382,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02638647469366841,"score_gpt":0.2137863467233641,"score_spread":0.1873998720296957,"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."}}