{"id":"W2906022401","doi":"10.1002/adts.201800144","title":"Polymerization Data Mining: A Perspective","year":2018,"lang":"en","type":"article","venue":"Advanced Theory and Simulations","topic":"Advanced Polymer Synthesis and Characterization","field":"Chemistry","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Perspective (graphical); Polymerization; Characterization (materials science); Field (mathematics); Data science; Nanotechnology; Systems engineering; Polymer; Artificial intelligence; Materials science; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006020488,0.0007901611,0.001447667,0.007067498,0.0006200147,0.007028039,0.002630042,0.002418856,0.002417838],"category_scores_gemma":[0.01018918,0.0007242927,0.001386245,0.007922122,0.001401769,0.007602134,0.001828541,0.00315688,0.001423723],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001535365,"about_ca_system_score_gemma":0.001619664,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001585438,"about_ca_topic_score_gemma":0.001051872,"domain_scores_codex":[0.996452,0.001194119,0.000331629,0.0007639197,0.001153158,0.0001051329],"domain_scores_gemma":[0.9881859,0.007885087,0.0005112257,0.001007576,0.001953668,0.0004565548],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000143649,0.0002553521,0.005917891,0.002943257,0.0003134702,0.0004101898,0.0002757778,0.01684701,0.001457607,0.1765883,0.05549664,0.7393508],"study_design_scores_gemma":[0.00004678124,0.0001622853,0.004248681,0.003026273,0.0001009551,0.001291842,0.0007210874,0.1272448,0.003881098,0.4366909,0.4224523,0.0001329825],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.007293364,0.2899792,0.6036773,0.072018,0.002572733,0.0002617869,0.003701632,0.001551617,0.01894448],"genre_scores_gemma":[0.1760663,0.2771377,0.5104128,0.008571538,0.01158842,0.0004245227,0.007401576,0.000307615,0.008089622],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.007067498,"threshold_uncertainty_score":0.03183973,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02035644286350695,"score_gpt":0.3056718272111196,"score_spread":0.2853153843476127,"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."}}