{"id":"W2552919194","doi":"10.1016/j.compchemeng.2016.11.015","title":"A quantile-based scenario analysis approach to biomass supply chain optimization under uncertainty","year":2016,"lang":"en","type":"article","venue":"Computers & Chemical Engineering","topic":"Forest Biomass Utilization and Management","field":"Engineering","cited_by":46,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Supply chain; Renewable energy; Mathematical optimization; Quantile; Computer science; Stochastic optimization; Stochastic programming; Supply chain optimization; Robust optimization; Supply chain management; Engineering; Mathematics; Econometrics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00008597093,0.0002506234,0.0002626477,0.0004640183,0.00002385301,0.00005562512,0.0002539346,0.00009542128,0.0000269775],"category_scores_gemma":[0.0000175924,0.0002143711,0.0001404578,0.001170163,0.00002108768,0.0000787084,0.00006930523,0.00006087569,0.00001467353],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002403659,"about_ca_system_score_gemma":0.000009220134,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007754703,"about_ca_topic_score_gemma":0.000001272119,"domain_scores_codex":[0.9988285,0.000008511738,0.0002658436,0.0003301628,0.0002027898,0.0003642489],"domain_scores_gemma":[0.9993657,0.00005123951,0.00002231352,0.0003140407,0.00003817719,0.0002085133],"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":[0.000006114402,0.00003053037,0.00009262126,0.00006914637,0.0002036932,0.000001357693,0.00002827878,0.9785277,0.01888262,0.0008316334,0.0005701501,0.0007561272],"study_design_scores_gemma":[0.0004421918,0.000009332658,0.0001877743,0.00004760248,0.00007119069,6.802788e-7,0.000004943302,0.9893188,0.008506255,0.00000338843,0.001094895,0.0003129793],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02375394,0.0000305823,0.9745961,0.0002022406,0.0002198868,0.0002017123,0.000009064541,0.0007971199,0.0001893188],"genre_scores_gemma":[0.9232328,0.000003285128,0.07640508,0.0001140518,0.00005576783,0.00003711614,0.00008647265,0.00004480654,0.00002060199],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8994789,"threshold_uncertainty_score":0.87418,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008694264061295535,"score_gpt":0.1896019191588517,"score_spread":0.1809076550975562,"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."}}