{"id":"W4283797345","doi":"10.1016/j.biortech.2022.127578","title":"Bioreactor-scale production of rhamnolipids from food waste digestate and its recirculation into anaerobic digestion for enhanced process performance: Creating closed-loop integrated biorefinery framework","year":2022,"lang":"en","type":"article","venue":"Bioresource Technology","topic":"Anaerobic Digestion and Biogas Production","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"Innovation and Technology Commission - Hong Kong; Innovation and Technology Commission","keywords":"Digestate; Biorefinery; Acidogenesis; Anaerobic digestion; Bioreactor; Biofuel; Pulp and paper industry; Bioprocess; Digestion (alchemy); Waste management; Methanogenesis; Biochemical engineering; Environmental science; Chemistry; Engineering; Methane; Chemical engineering; Chromatography","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002298668,0.0002803123,0.0003308684,0.0006076013,0.0003171067,0.00002070297,0.0002016896,0.000309377,0.00001718144],"category_scores_gemma":[0.0001942513,0.0002850091,0.00005536214,0.001183691,0.0001466486,0.0001947883,0.0000775646,0.0004337662,0.000001605244],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001865888,"about_ca_system_score_gemma":0.00003482168,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002002612,"about_ca_topic_score_gemma":0.00001211409,"domain_scores_codex":[0.9984249,0.0000383483,0.0004840718,0.0005310891,0.000207265,0.0003142636],"domain_scores_gemma":[0.9991881,0.00004240639,0.0002308625,0.0003130562,0.0001724853,0.00005309431],"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":[0.0002452737,0.0001534619,0.00547642,0.000252915,0.0001113646,4.532521e-7,0.002358908,0.02066221,0.8984866,0.001504637,0.0000628536,0.07068491],"study_design_scores_gemma":[0.0006554711,0.0009844316,0.003754299,0.0002585202,0.00008942393,0.00001370901,0.004889689,0.01102921,0.9744213,0.001070153,0.00230908,0.0005247021],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9936418,0.0006884783,0.003076276,0.0003082147,0.0006288738,0.0007061585,0.00005298483,0.000865172,0.00003206474],"genre_scores_gemma":[0.9966693,0.0002229235,0.002186197,0.00001018377,0.0001744979,0.0003275236,0.000303459,0.00005853233,0.00004740535],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07593472,"threshold_uncertainty_score":0.9999602,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008481549448953562,"score_gpt":0.2154068069770479,"score_spread":0.2069252575280944,"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."}}