{"id":"W2168263295","doi":"10.1002/mren.200600028","title":"Exploring Reaction Kinetics of a Multi‐Site Ziegler‐Natta Catalyst Using Deconvolution of Molecular Weight Distributions for Ethylene‐Hexene Copolymers","year":2007,"lang":"en","type":"article","venue":"Macromolecular Reaction Engineering","topic":"Organometallic Complex Synthesis and Catalysis","field":"Chemistry","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Natta; Copolymer; Polymer; Molar mass distribution; Ethylene; 1-Hexene; Polymer chemistry; Catalysis; Deconvolution; Materials science; Polymerization; Kinetics; Chemistry; Chemical engineering; Organic chemistry; Mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001942961,0.0002141994,0.0001555558,0.0003284665,0.00009392476,0.0002837908,0.0001835991,0.0001749913,0.0007367578],"category_scores_gemma":[0.0002627015,0.0001697566,0.0002001098,0.0001612291,0.0001437833,0.0003188143,0.00008803371,0.000321911,0.0001919802],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002906551,"about_ca_system_score_gemma":0.0001390785,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0013589,"about_ca_topic_score_gemma":0.001515662,"domain_scores_codex":[0.9999304,0.000005203958,0.000003405861,0.00001802804,0.00002836125,0.00001460274],"domain_scores_gemma":[0.9999118,0.00003980257,0.00001726832,0.000008603593,0.00001297504,0.000009551014],"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":[0.000041302,0.000007940745,0.0003901897,0.00001040361,0.000002920758,0.00001352257,0.00001632609,0.000559877,0.9968047,0.00007005314,0.000007151337,0.002075587],"study_design_scores_gemma":[0.000001739276,0.00001743455,0.002033364,0.000001068487,0.000003262548,0.00002580424,0.000007082902,0.008461176,0.9892949,0.00001747895,0.0001339144,0.000002605552],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9889159,0.0001867376,0.01022784,0.00001340756,0.00000275048,0.000007224978,0.00006685279,0.00006384475,0.0005154429],"genre_scores_gemma":[0.9928276,0.0001416277,0.006103349,0.000003745251,6.742491e-7,0.000006275568,0.0000821111,0.00001744378,0.0008170714],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0013589,"threshold_uncertainty_score":0.002702057,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03636616624572235,"score_gpt":0.2474519443805585,"score_spread":0.2110857781348361,"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."}}