{"id":"W3143137145","doi":"10.1007/978-981-15-9267-6_7","title":"Resource Utilization of Agricultural/Forestry Residues via Fractionation into Cellulose, Hemicellulose and Lignin","year":2021,"lang":"en","type":"book-chapter","venue":"Green chemistry and sustainable technology","topic":"Biofuel production and bioconversion","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"","keywords":"Hemicellulose; Lignin; Cellulose; Fractionation; Agriculture; Forestry; Pulp and paper industry; Resource (disambiguation); Chemistry; Agroforestry; Business; Environmental science; Organic chemistry; Geography; Engineering; Computer science; Biology; Ecology","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":[0.00005641577,0.0004071906,0.0002122495,0.0002929343,0.0002104388,0.0005417035,0.0002875356,0.000185815,0.003626564],"category_scores_gemma":[0.00002632654,0.0001362948,0.0003259848,0.0004464416,0.0001468104,0.0005461973,0.0001986915,0.0003884173,0.002521617],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003212846,"about_ca_system_score_gemma":0.0002617547,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001015268,"about_ca_topic_score_gemma":0.002744777,"domain_scores_codex":[0.9999691,0.000001816464,0.000001237972,0.00000803813,0.00001337187,0.000006429915],"domain_scores_gemma":[0.9999932,0.000002283186,6.508027e-7,0.000001091535,0.00000185738,9.185075e-7],"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":[0.00005552952,0.0001400195,0.0001974433,0.0005254124,0.0000134014,0.0002644059,0.0001548317,0.0018074,0.6595346,0.01490043,0.004184092,0.3182225],"study_design_scores_gemma":[0.000006197702,0.0001472829,0.001746422,0.0001287619,0.00002223698,0.0006105992,0.0001041949,0.002751238,0.7430328,0.008064681,0.2433678,0.00001788974],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2167325,0.07544757,0.100183,0.0007926977,0.0006945914,0.00021693,0.0009557775,0.0006250083,0.6043519],"genre_scores_gemma":[0.3073346,0.05493293,0.04206647,0.0003736531,0.0001185928,0.0001004831,0.001048827,0.0002661612,0.5937583],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003626564,"threshold_uncertainty_score":0.01213205,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005586454372682139,"score_gpt":0.1827748465285127,"score_spread":0.1771883921558306,"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."}}