{"id":"W2275719568","doi":"10.1021/acssuschemeng.5b01242","title":"Improving Sugar Yields and Reducing Enzyme Loadings in the Deacetylation and Mechanical Refining (DMR) Process through Multistage Disk and Szego Refining and Corresponding Techno-Economic Analysis","year":2015,"lang":"en","type":"article","venue":"ACS Sustainable Chemistry & Engineering","topic":"Biofuel production and bioconversion","field":"Engineering","cited_by":56,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Office of Energy Efficiency and Renewable Energy","keywords":"Refining (metallurgy); Biorefinery; Sugar; Xylose; Pulp and paper industry; Chemistry; Reducing sugar; Enzymatic hydrolysis; Biomass (ecology); Process (computing); Hydrolysis; Materials science; Organic chemistry; Raw material; Computer science; Agronomy","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":[0.0001391706,0.0003640582,0.0002025031,0.0002672952,0.0001008951,0.0002821113,0.0002030435,0.0001520614,0.0007963265],"category_scores_gemma":[0.0001540427,0.0001184301,0.0002418359,0.0003419293,0.0001264747,0.000252789,0.0001615284,0.0002986974,0.0001936111],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002422044,"about_ca_system_score_gemma":0.0002104253,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001005708,"about_ca_topic_score_gemma":0.00305442,"domain_scores_codex":[0.9999001,0.000006087597,0.00001054145,0.00002220567,0.0000407984,0.00002036426],"domain_scores_gemma":[0.9999585,0.000007726324,0.0000126993,0.000005477018,0.00001188338,0.00000363755],"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":[0.00003041617,0.000009839986,0.0002569733,0.00001773436,0.000002259259,0.000021249,0.000008778087,0.000107105,0.9962605,0.00006025251,0.0000123432,0.003212534],"study_design_scores_gemma":[0.000001580049,0.00003046776,0.00196752,5.949804e-7,0.000005594563,0.00002396149,0.000006649438,0.0008099566,0.9968145,0.00001145437,0.0003256599,0.000002098529],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9845748,0.0003693252,0.01383744,0.0000334041,0.000009551423,0.00002316656,0.0001306242,0.00006755695,0.0009542515],"genre_scores_gemma":[0.9779613,0.0005842137,0.0190741,0.00001425818,0.000003471199,0.00001880933,0.0003003317,0.00002925102,0.002014173],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001005708,"threshold_uncertainty_score":0.00266397,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007723508974148742,"score_gpt":0.2097321695109057,"score_spread":0.2020086605367569,"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."}}