{"id":"W4406892647","doi":"10.1039/d4se01347f","title":"Advancing hydrothermal liquefaction of Canadian forestry biomass for sustainable biocrude production: co-solvent integration, co-liquefaction, and process optimization","year":2025,"lang":"en","type":"article","venue":"Sustainable Energy & Fuels","topic":"Thermochemical Biomass Conversion Processes","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Liquefaction; Hydrothermal liquefaction; Biomass (ecology); Hydrothermal circulation; Process (computing); Biofuel; Production (economics); Environmental science; Waste management; Sustainable production; Forestry; Business; Pulp and paper industry; Chemistry; Chemical engineering; Engineering; Ecology; Organic chemistry; Computer science; Economics; Geography; Biology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002583084,0.0004131235,0.0002652617,0.0003718311,0.0007022491,0.0007280948,0.0003101025,0.0003044347,0.001243923],"category_scores_gemma":[0.0002698612,0.0001688536,0.0002907113,0.000542016,0.0002611826,0.000584032,0.0004445518,0.0008314518,0.0002739629],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00343559,"about_ca_system_score_gemma":0.004028483,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1964214,"about_ca_topic_score_gemma":0.5209396,"domain_scores_codex":[0.9998124,0.000007204217,0.000008559593,0.00002354669,0.00009435082,0.00005389901],"domain_scores_gemma":[0.99993,0.000008411927,0.000007823501,0.000004491296,0.00003697081,0.00001228616],"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":[0.0001255793,0.00009787382,0.0008592378,0.0002029881,0.00001667814,0.00009737239,0.0001044209,0.00329011,0.967464,0.0009821048,0.0003883275,0.0263711],"study_design_scores_gemma":[0.00001471358,0.0001384339,0.002484847,0.00001458995,0.00002967533,0.00004021912,0.0001205014,0.006371451,0.9846506,0.0001186727,0.005998384,0.00001796506],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9732583,0.002938552,0.01129031,0.0004380395,0.000055985,0.0001154902,0.000475093,0.0001247554,0.01130342],"genre_scores_gemma":[0.9826683,0.003547574,0.008888312,0.00004596844,0.000008213377,0.00003080147,0.0004029529,0.00003055813,0.00437742],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8035786,"threshold_uncertainty_score":0.3905563,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004046001693079461,"score_gpt":0.2228750104684027,"score_spread":0.2188290087753232,"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."}}