{"id":"W3046330458","doi":"10.1002/mats.202000048","title":"Using Artificial Intelligence Techniques to Design Ethylene/1‐Olefin Copolymers","year":2020,"lang":"en","type":"article","venue":"Macromolecular Theory and Simulations","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Thailand Research Fund","keywords":"Comonomer; Polymer; Branching (polymer chemistry); Molar mass distribution; Copolymer; Materials science; Polymerization; Olefin fiber; Polymer chemistry; Chemical engineering; Composite material; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001168379,0.0001742178,0.0001908212,0.00007714958,0.0003974922,0.0002237183,0.0003043324,0.00006829883,0.0008609631],"category_scores_gemma":[0.0008416385,0.0001701301,0.00003550146,0.0003386194,0.0001961921,0.0001406512,0.0001805434,0.0001095234,0.0001048346],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002010285,"about_ca_system_score_gemma":0.00004592354,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001777512,"about_ca_topic_score_gemma":0.00000129136,"domain_scores_codex":[0.9979369,0.0007899994,0.0003068088,0.0004534856,0.0002242677,0.0002885385],"domain_scores_gemma":[0.9991057,0.0003164398,0.00008223542,0.0002299274,0.000056733,0.0002089601],"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":[0.00007679368,0.00001255466,0.00001112413,0.000008952721,0.00000240162,0.00001055363,0.0007365668,0.1392,0.8055441,0.05290328,0.000003905145,0.001489706],"study_design_scores_gemma":[0.00002522839,0.0001084244,0.000009151224,0.00001908454,0.00001703571,0.000007416781,0.0001093239,0.1079751,0.8486433,0.04271717,0.000151989,0.0002167053],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3092674,0.00003032593,0.6896054,0.0005104305,0.00007712617,0.0002432243,0.00001656157,0.0001349265,0.0001146506],"genre_scores_gemma":[0.8717755,0.000001248708,0.1264631,0.001650327,0.00006918698,0.000008308279,0.000002835234,0.00001990558,0.000009550169],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5631422,"threshold_uncertainty_score":0.9426942,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06703884889636122,"score_gpt":0.3338757628935188,"score_spread":0.2668369139971576,"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."}}