{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000142668,0.0001149698,0.0001214735,0.0001742751,0.0000473841,0.00005105776,0.00003643814,0.00005227774,0.00002341666],"category_scores_gemma":[0.000009783736,0.0001053729,0.00001588248,0.0001591941,0.00002845101,0.00005683364,0.00005865475,0.00006940735,0.000002426621],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002920658,"about_ca_system_score_gemma":0.00000530677,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005141448,"about_ca_topic_score_gemma":0.00002230468,"domain_scores_codex":[0.9994089,0.00001010921,0.0001721333,0.0001516885,0.00008598914,0.0001711958],"domain_scores_gemma":[0.9998301,0.00002550137,0.00001258302,0.00007251348,0.00001611948,0.00004324454],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001789364,0.00002480046,0.00001337771,0.0009378627,0.0000610277,0.000009090874,0.001376394,0.2353106,0.03789917,0.7015427,0.00007506964,0.02273208],"study_design_scores_gemma":[0.0001725008,0.00002717311,0.000004328949,0.00006691303,0.00001449382,0.000002580353,0.000983397,0.9818546,0.01364761,0.001639854,0.001481277,0.0001052751],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02201787,0.001640526,0.9726278,0.00002733568,0.0000216473,0.0001261846,9.335736e-7,0.000250216,0.003287553],"genre_scores_gemma":[0.9911906,0.000245785,0.007232476,0.000007336926,0.00001142054,0.00004332407,0.00001632077,0.00002914959,0.001223571],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9691728,"threshold_uncertainty_score":0.4296983,"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."}}