{"id":"W2789661278","doi":"10.1002/masy.201700060","title":"Monte Carlo Simulation of Olefin Block Copolymers: Bivariate Distribution of Molecular Weight and Chemical Composition","year":2018,"lang":"en","type":"article","venue":"Macromolecular Symposia","topic":"Polymer crystallization and properties","field":"Materials Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Kasetsart University","keywords":"Monte Carlo method; Polyolefin; Copolymer; Molar mass distribution; Materials science; Polymerization; Olefin fiber; Bivariate analysis; Block (permutation group theory); Work (physics); Polymer chemistry; Thermodynamics; Mathematics; Polymer; Nanotechnology; Physics; Composite material; Statistics; Geometry","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":[0.0006624685,0.000336966,0.000600535,0.0004388412,0.0005312458,0.0005024888,0.0005608102,0.0008855677,0.001169802],"category_scores_gemma":[0.00185814,0.0004075213,0.0004729011,0.0005552756,0.0006088995,0.0004345137,0.0002988917,0.0005755987,0.0001092726],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008498001,"about_ca_system_score_gemma":0.001218244,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01441982,"about_ca_topic_score_gemma":0.009935105,"domain_scores_codex":[0.9998105,0.00007123366,0.000007402627,0.00001997387,0.00004663027,0.00004434829],"domain_scores_gemma":[0.998733,0.0008878111,0.0001161845,0.00006360537,0.0001141988,0.00008525189],"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":[0.00005603106,0.00003519799,0.0008317364,0.00001829384,0.00001764949,0.00003857225,0.00002022226,0.9928795,0.001454474,0.003617316,0.0001177579,0.0009133596],"study_design_scores_gemma":[0.00000775247,0.000007349249,0.0001281829,0.000001993722,0.000002724388,0.000003786805,0.000003011021,0.9990163,0.0004218717,0.0002937526,0.0001107038,0.000002552951],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9037379,0.0006260453,0.08221377,0.0003902193,0.00005849907,0.00009044258,0.0003489695,0.0002274505,0.01230667],"genre_scores_gemma":[0.9852587,0.0002440951,0.01243888,0.00006632892,0.00001186492,0.0001199489,0.0002096809,0.0000443706,0.001606124],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01441982,"threshold_uncertainty_score":0.0286718,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006388782031617588,"score_gpt":0.2253241375494658,"score_spread":0.2189353555178482,"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."}}