{"id":"W4381948346","doi":"10.1016/j.renene.2023.118956","title":"Development of a global kinetic model based on chemical compositions of lignocellulosic biomass for predicting product yields from hydrothermal liquefaction","year":2023,"lang":"en","type":"article","venue":"Renewable Energy","topic":"Lignin and Wood Chemistry","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":false,"ca_institutions":"Natural Resources Canada; Western University","funders":"","keywords":"Lignocellulosic biomass; Hemicellulose; Biomass (ecology); Lignin; Hydrothermal liquefaction; Cellulose; Pulp and paper industry; Chemistry; Biofuel; Environmental science; Waste management; Organic chemistry; Catalysis; Agronomy; Engineering","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":[0.0003068305,0.001062921,0.0009339578,0.0003365396,0.0005328394,0.0007785514,0.000963149,0.00109517,0.001297881],"category_scores_gemma":[0.0005909425,0.0006101269,0.0009871747,0.0003584409,0.0003240458,0.0008492157,0.0005583059,0.0007288088,0.0003735066],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008021329,"about_ca_system_score_gemma":0.001772838,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02489617,"about_ca_topic_score_gemma":0.01276075,"domain_scores_codex":[0.9999352,0.00001059286,0.000005769333,0.00002082147,0.00001620833,0.00001155248],"domain_scores_gemma":[0.9998133,0.00007968157,0.00001746438,0.00001865923,0.00005225894,0.00001867055],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001137246,0.00001744726,0.0005385276,0.00001268462,0.00001198937,0.00002091233,0.000006435552,0.9943849,0.002351649,0.0002749284,0.00005948647,0.002309615],"study_design_scores_gemma":[0.000003674978,0.000006479602,0.00009987059,7.362408e-7,0.000003099636,0.000001603115,0.000001669117,0.9991186,0.0005896579,0.00008797878,0.00008447211,0.000002161111],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4482667,0.0003672393,0.5384089,0.0003182989,0.000144439,0.0001594129,0.001330037,0.002270439,0.008734518],"genre_scores_gemma":[0.9512829,0.0002253498,0.04440284,0.00003840357,0.00001900724,0.0001908165,0.001054896,0.0001837859,0.002601919],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02489617,"threshold_uncertainty_score":0.04950255,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01221709026793762,"score_gpt":0.2124067663778727,"score_spread":0.200189676109935,"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."}}