{"id":"W2745939307","doi":"10.1002/mats.201700040","title":"Copolymerization of Ethylene with 1,9‐Decadiene: Part II—Prediction of Molecular Weight Distributions","year":2017,"lang":"en","type":"article","venue":"Macromolecular Theory and Simulations","topic":"Polymer crystallization and properties","field":"Materials Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Conselho Nacional de Desenvolvimento Científico e Tecnológico","keywords":"Macromonomer; Radius of gyration; Copolymer; Monte Carlo method; Materials science; Molar mass distribution; Polymer; Ethylene; Polymer chemistry; Gyration; Thermodynamics; Polymer science; Statistical physics; Chemistry; Mathematics; Physics; Catalysis; Geometry; Organic chemistry; Composite material","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":[0.0002168997,0.0002667778,0.0001813733,0.0001981156,0.0001522543,0.0002147569,0.0001825503,0.0002908233,0.000453297],"category_scores_gemma":[0.0005418079,0.0001867387,0.0001671986,0.0001461665,0.0001774914,0.0002886028,0.0001350921,0.0003017238,0.00008421612],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005126599,"about_ca_system_score_gemma":0.0004845379,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003866543,"about_ca_topic_score_gemma":0.003422198,"domain_scores_codex":[0.999961,0.000007920249,0.000001990969,0.000008868794,0.00001525969,0.000004857062],"domain_scores_gemma":[0.9998172,0.0001124784,0.00002844249,0.00001600412,0.00001777046,0.000008080777],"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":[0.00008634037,0.00005138611,0.002404472,0.00005287988,0.000009378717,0.00005301065,0.0000476256,0.8941938,0.08756964,0.001833692,0.00005954875,0.01363823],"study_design_scores_gemma":[0.000004026661,0.00001546461,0.0004529353,0.000001176572,0.000001297633,0.000007285309,0.000003370989,0.9781078,0.02115253,0.0001316198,0.0001200142,0.000002517051],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8838435,0.0001894783,0.1147279,0.00003798156,0.000008903367,0.00003302202,0.00005947843,0.00008803018,0.001011692],"genre_scores_gemma":[0.9679024,0.0001825866,0.0311567,0.000006742283,0.000002289429,0.00003475013,0.00007498645,0.00002646007,0.0006131006],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003866543,"threshold_uncertainty_score":0.007688105,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009902659512893255,"score_gpt":0.2323240988291009,"score_spread":0.2224214393162076,"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."}}