{"id":"W2809203089","doi":"10.1002/mren.201800020","title":"A Simple Monte Carlo Method for Modeling Arborescent Polymer Production in Continuous Stirred Tank Reactor","year":2018,"lang":"en","type":"article","venue":"Macromolecular Reaction Engineering","topic":"Advanced Polymer Synthesis and Characterization","field":"Chemistry","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Continuous stirred-tank reactor; Monte Carlo method; Monomer; Copolymer; Chemistry; Materials science; Batch reactor; Inflow; Polymer chemistry; Polymer; Thermodynamics; Mechanics; Physical chemistry; Physics; Organic chemistry; Mathematics; Catalysis","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001373834,0.0002260168,0.0002461921,0.0001249898,0.00005793797,0.0000355507,0.00009403862,0.0001228002,0.00002647717],"category_scores_gemma":[0.0001466989,0.0002566435,0.00009254307,0.0001671229,0.00001137849,0.0001866077,0.00002374792,0.0001538263,0.000003071742],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001563445,"about_ca_system_score_gemma":0.0000189346,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001477752,"about_ca_topic_score_gemma":0.00002027599,"domain_scores_codex":[0.9987073,0.00001428411,0.0003495076,0.0004270726,0.0001603769,0.0003414562],"domain_scores_gemma":[0.9993892,0.00002596264,0.0001122886,0.0003075529,0.00009337139,0.00007164104],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00007330046,0.00004490941,0.00004692701,0.00006458611,0.00003160897,0.000004618149,0.0001444165,0.004669137,0.9855213,0.00002451314,0.000008755762,0.009365964],"study_design_scores_gemma":[0.0002508038,0.00001406059,0.00003947386,0.00006520037,0.00002278005,0.00001180629,0.0000561572,0.3279312,0.669001,0.000008490273,0.002401057,0.0001979018],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6187972,0.0002009407,0.3800804,0.00009122267,0.0003398507,0.0001923528,0.00001928951,0.0001883544,0.00009037478],"genre_scores_gemma":[0.9949442,0.00001702476,0.004048863,0.00002158493,0.0005195263,0.0001325988,0.00005360976,0.00008437151,0.0001782571],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3761469,"threshold_uncertainty_score":0.9999886,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009592349115965215,"score_gpt":0.2472333981391777,"score_spread":0.2376410490232125,"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."}}