{"id":"W2731487300","doi":"10.15017/1809681","title":"Modeling and Optimization of Biomass Supply Chain for Energy, Chemicals and Materials Productions","year":2016,"lang":"en","type":"article","venue":"Kyushu University Institutional Repository (QIR) (Kyushu University)","topic":"Forest Biomass Utilization and Management","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Supply chain; Biomass (ecology); Environmental science; Process engineering; Pulp and paper industry; Business; Engineering; Agronomy; Biology","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":[0.00006579429,0.000158614,0.0001784728,0.0004065335,0.0003320433,0.0000205199,0.0001361774,0.0001151148,0.00001373241],"category_scores_gemma":[0.00001864609,0.0001666542,0.00004817229,0.0002688966,0.0002737302,0.0004701787,0.0001054055,0.00002895972,6.413821e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002225407,"about_ca_system_score_gemma":0.00005895141,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007245774,"about_ca_topic_score_gemma":0.00002153935,"domain_scores_codex":[0.9992197,0.00002777518,0.0001601067,0.0002900023,0.0001300207,0.0001724191],"domain_scores_gemma":[0.9994951,0.00002917628,0.00005857835,0.0001584613,0.0001540006,0.0001046308],"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":[0.0004704406,0.0001662021,0.0005883686,0.0005135793,0.0004158065,0.00008198299,0.0002423421,0.1983988,0.2872637,0.5100464,0.0009062439,0.0009061816],"study_design_scores_gemma":[0.01166992,0.0003602047,0.0009360856,0.0008680955,0.0008661561,0.000194109,0.002337821,0.3748672,0.3690417,0.000973654,0.2356592,0.00222576],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3572353,0.00009418933,0.6363214,0.0002518168,0.0004166226,0.0003488388,0.0002024392,0.0002301184,0.004899212],"genre_scores_gemma":[0.9930896,0.0003602348,0.004038507,0.000006589731,0.00004996239,7.845189e-7,0.00005189547,0.00001217968,0.002390307],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6358542,"threshold_uncertainty_score":0.6795961,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007202393116048883,"score_gpt":0.1583046591746415,"score_spread":0.1511022660585926,"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."}}