{"id":"W4402678577","doi":"10.1016/j.apenergy.2024.123794","title":"Designing a resilient and sustainable multi-feedstock bioethanol supply chain: Integration of mathematical modeling and machine learning","year":2024,"lang":"en","type":"article","venue":"Applied Energy","topic":"Forest Biomass Utilization and Management","field":"Engineering","cited_by":37,"is_retracted":false,"has_abstract":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal","funders":"","keywords":"Raw material; Supply chain; Biofuel; Biochemical engineering; Manufacturing engineering; Sustainability; Process engineering; Chain (unit); Sustainable design; Engineering; Computer science; Waste management; Business; Chemistry","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":[0.0007524061,0.0006603795,0.0006474803,0.0006293872,0.0007539256,0.001599218,0.001105984,0.00163797,0.002074024],"category_scores_gemma":[0.001264454,0.0007251315,0.0006474371,0.0005627676,0.0006135119,0.00216904,0.001341198,0.000969801,0.0004147623],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001036334,"about_ca_system_score_gemma":0.002637715,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004381076,"about_ca_topic_score_gemma":0.005055746,"domain_scores_codex":[0.9997367,0.00006253686,0.00001370306,0.00007265813,0.00006735895,0.00004698262],"domain_scores_gemma":[0.9995896,0.0001906409,0.0000863401,0.00002912463,0.00007511402,0.00002913808],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001323001,0.00003453,0.0003691457,0.00003711761,0.0000137905,0.0000489751,0.00002011397,0.9864812,0.002723119,0.003928167,0.00007575285,0.00625478],"study_design_scores_gemma":[0.000002617616,0.00001815323,0.00004539145,0.000005066257,0.000004103821,0.000006767089,0.00001426996,0.9966498,0.0007316396,0.002315093,0.0002037486,0.000003378642],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07230772,0.0001996356,0.9220557,0.0005252935,0.00003142049,0.0001158935,0.00006485503,0.0002242973,0.00447529],"genre_scores_gemma":[0.8634509,0.000373128,0.1318202,0.00008213075,0.00001613325,0.0002082432,0.0001036729,0.00005125152,0.003894323],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004381076,"threshold_uncertainty_score":0.008711159,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01418496584094308,"score_gpt":0.216641894159045,"score_spread":0.2024569283181019,"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."}}