{"id":"W3199410397","doi":"10.1002/cssc.202101492","title":"Engineered Sorghum Bagasse Enables a Sustainable Biorefinery with <i>p</i>‐Hydroxybenzoic Acid‐Based Deep Eutectic Solvent","year":2021,"lang":"en","type":"article","venue":"ChemSusChem","topic":"Lignin and Wood Chemistry","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Lawrence Berkeley National Laboratory; Oak Ridge National Laboratory; Biological and Environmental Research; National Institute of Food and Agriculture; Battelle; Division of Chemical, Bioengineering, Environmental, and Transport Systems; Korea Institute of Science and Technology; UT-Battelle; Center for Bioenergy Innovation; Office of Science; U.S. Department of Agriculture; U.S. Department of Energy; National Science Foundation","keywords":"Biorefinery; Lignin; Deep eutectic solvent; Biomass (ecology); Chemistry; Bagasse; Cellulose; Pulp and paper industry; Biofuel; Organic chemistry; Lignocellulosic biomass; Biotechnology; Raw material; Eutectic system; Agronomy; Biology","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":[0.000083654,0.0004017243,0.0001370539,0.0001288914,0.0001077301,0.0002927361,0.0001664857,0.0001876208,0.0005726358],"category_scores_gemma":[0.00005818969,0.000141195,0.0001828357,0.0001464092,0.0001394519,0.0002047049,0.0002158385,0.000304216,0.0002870259],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000204687,"about_ca_system_score_gemma":0.0001840516,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005150749,"about_ca_topic_score_gemma":0.000883792,"domain_scores_codex":[0.9999377,0.00000788987,0.000005665874,0.0000161122,0.00001846142,0.00001416402],"domain_scores_gemma":[0.9999557,0.000004344109,0.00001664511,0.00000673562,0.000006167692,0.00001043018],"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":[0.000007098036,0.000004058735,0.00004324024,0.000007954633,0.000001583195,0.00001495701,0.000002321633,0.00004417633,0.9995466,0.00005005934,0.000005813648,0.0002721595],"study_design_scores_gemma":[0.000001937108,0.00004460736,0.0006629534,0.000001566353,0.000004960676,0.00004564507,0.000009051366,0.0005363392,0.9977289,0.00002867646,0.0009328283,0.00000258976],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9883097,0.0002680219,0.009899921,0.00005120798,0.00001884054,0.00001376274,0.0001913153,0.0001357132,0.001111436],"genre_scores_gemma":[0.9934188,0.0001853764,0.00483149,0.00001561073,0.000002540788,0.000007585774,0.000204864,0.00002669311,0.001307024],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0005726358,"threshold_uncertainty_score":0.001915634,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.004335871232124872,"score_gpt":0.1665991874092792,"score_spread":0.1622633161771543,"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."}}