{"id":"W2111591386","doi":"10.1002/mren.201400048","title":"Design of Optimal Experiments for Terpolymerization Reactivity Ratio Estimation","year":2015,"lang":"en","type":"article","venue":"Macromolecular Reaction Engineering","topic":"Optimal Experimental Design Methods","field":"Decision Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Reactivity (psychology); Heuristics; Chemistry; Mathematics; Optimal design; Composition (language); Thermodynamics; Statistics; Mathematical optimization; Physics","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":[],"consensus_categories":[],"category_scores_codex":[0.002124195,0.0001893308,0.0002948287,0.0003059906,0.00005115634,0.0001012922,0.0002458117,0.00009649723,0.00001239708],"category_scores_gemma":[0.002238271,0.0001871952,0.00009650467,0.0005095961,0.00002974211,0.0006545818,0.00004980368,0.00007973691,0.00001637866],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001448642,"about_ca_system_score_gemma":0.00006737548,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001590068,"about_ca_topic_score_gemma":5.591676e-8,"domain_scores_codex":[0.9978208,0.0001903292,0.0005706114,0.0003905498,0.0008119697,0.0002157183],"domain_scores_gemma":[0.9985204,0.0003815387,0.0002928581,0.0004006801,0.0002673405,0.0001371419],"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":[0.00008726558,0.00004520078,0.0000061013,0.000003793599,0.00001285565,0.00000159893,0.0003040782,0.2714637,0.721862,0.00023241,0.00004008811,0.005940867],"study_design_scores_gemma":[0.0003155725,0.000116741,0.00003566077,0.000007619866,0.000007764877,0.000009829817,0.00009356228,0.4705185,0.5284651,0.0001191715,0.0002100407,0.0001004143],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06180751,0.00008530036,0.9368488,0.00002509035,0.0004501522,0.0005468911,0.000004588542,0.00008225042,0.0001494479],"genre_scores_gemma":[0.6163675,9.27878e-7,0.3834133,0.000009846363,0.00002746694,0.00008407344,0.00001292817,0.00002597013,0.00005799038],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5545599,"threshold_uncertainty_score":0.7633597,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1416078855675469,"score_gpt":0.4026099270860546,"score_spread":0.2610020415185078,"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."}}