{"id":"W4413092842","doi":"10.1007/978-981-96-5198-6_7","title":"Biocrude Production and Subsequent Upgradation via Hydrothermal Liquefaction, Distillation, and Hydrotreatment of Agro-Forestry Biomass: A Canadian Context","year":2025,"lang":"en","type":"book-chapter","venue":"Advances in sustainability science and technology","topic":"Thermochemical Biomass Conversion Processes","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Hydrothermal liquefaction; Biomass (ecology); Context (archaeology); Distillation; Liquefaction; Forestry; Production (economics); Environmental science; Waste management; Pulp and paper industry; Biofuel; Chemistry; Engineering; Geography; Agronomy; Organic chemistry; Biology; Economics; Archaeology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.000201509,0.0003301584,0.0002024247,0.0006590714,0.001147609,0.001452963,0.0006422624,0.0003461062,0.002822173],"category_scores_gemma":[0.0001266505,0.0001550949,0.0002329974,0.001612922,0.0009378586,0.0007471008,0.0003618347,0.0006153563,0.0003886722],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01599776,"about_ca_system_score_gemma":0.01169701,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.787634,"about_ca_topic_score_gemma":0.9397458,"domain_scores_codex":[0.9998013,0.000004396843,0.000003406188,0.00002067543,0.0001265861,0.00004363624],"domain_scores_gemma":[0.9999596,0.000004718272,0.000002464445,0.000003177431,0.00002348834,0.000006521343],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001504927,0.0001829396,0.002240606,0.0007522053,0.00002297008,0.0009365584,0.002034791,0.006367917,0.3234804,0.1621744,0.0174573,0.4841994],"study_design_scores_gemma":[0.00001385451,0.0001000688,0.01145405,0.0001892904,0.00003447998,0.0004507494,0.001238686,0.003795671,0.2688906,0.006620168,0.7071555,0.00005683616],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.3191182,0.06299232,0.01200142,0.003207669,0.0003518266,0.0001374547,0.0007787698,0.0001939602,0.6012183],"genre_scores_gemma":[0.5372542,0.06833967,0.01432602,0.0002775314,0.00003515101,0.00003212367,0.0005545619,0.00009850095,0.3790823],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.212366,"threshold_uncertainty_score":0.4272333,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003337973704313353,"score_gpt":0.2132108122195534,"score_spread":0.2098728385152401,"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."}}