{"id":"W4385242124","doi":"10.1016/j.energy.2023.128546","title":"Yield prediction and optimization of biomass-based products by multi-machine learning schemes: Neural, regression and function-based techniques","year":2023,"lang":"en","type":"article","venue":"Energy","topic":"Thermochemical Biomass Conversion Processes","field":"Engineering","cited_by":51,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Biofuel; Biomass (ecology); Pyrolysis; Yield (engineering); Radial basis function; Overfitting; Bioenergy; Artificial neural network; Mathematics; Pulp and paper industry; Biological system; Process engineering; Environmental science; Computer science; Machine learning; Engineering; Materials science; Waste management; 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":[0.0009513018,0.0005074929,0.0005207113,0.0004072903,0.0002148922,0.0005557071,0.0004485291,0.0005752797,0.0004321721],"category_scores_gemma":[0.001469071,0.0002385242,0.0004923056,0.000605955,0.0003019454,0.0008143247,0.0004166414,0.0005037932,0.0001472717],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005556758,"about_ca_system_score_gemma":0.000295028,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001886918,"about_ca_topic_score_gemma":0.001837503,"domain_scores_codex":[0.9998268,0.00006029194,0.0000122653,0.00003369194,0.00005398837,0.00001289293],"domain_scores_gemma":[0.9996375,0.0001936844,0.00006027041,0.00002965546,0.00007057339,0.000008276412],"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":[0.0001144902,0.00007120088,0.0009556948,0.00006613982,0.0000238449,0.00001974789,0.00002201403,0.9233442,0.01615494,0.001605205,0.0001048141,0.05751758],"study_design_scores_gemma":[0.000001348344,0.00001393185,0.0001360902,0.000001134586,0.000002717477,0.000001754206,0.000001126195,0.996505,0.003092276,0.0001995812,0.00004333921,0.000001723989],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3560097,0.001097302,0.6400979,0.0001988395,0.00004157841,0.00003267367,0.00007344988,0.0002528395,0.00219573],"genre_scores_gemma":[0.94275,0.0003381266,0.05553748,0.00001540974,0.00001254075,0.00003449651,0.0000612744,0.00003471866,0.001215811],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001886918,"threshold_uncertainty_score":0.005031049,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01055532443621461,"score_gpt":0.2049298630318815,"score_spread":0.1943745385956669,"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."}}