{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00006996792,0.0001164441,0.0001067166,0.00003965434,0.0003187327,0.00002790065,0.0001511984,0.0000531864,0.0007222922],"category_scores_gemma":[0.0002920152,0.0001153909,0.00001523886,0.0001210143,0.000161834,0.0005297997,0.00008949432,0.00005067111,0.000004631029],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001603634,"about_ca_system_score_gemma":0.00001875661,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001606943,"about_ca_topic_score_gemma":0.000005894055,"domain_scores_codex":[0.9992568,0.00003368349,0.0001532503,0.0003344892,0.00007805292,0.0001437327],"domain_scores_gemma":[0.9989851,0.0002996659,0.00008985391,0.000490201,0.00007928846,0.00005589488],"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":[0.0003829889,0.0001200809,0.001759687,0.00002095678,0.00008470476,0.000001818881,0.004150776,0.0002500571,0.5365481,0.3253204,0.00001147762,0.131349],"study_design_scores_gemma":[0.003276111,0.0002015768,0.002569633,0.0002953147,0.0003585295,0.00003554707,0.01890703,0.09013583,0.6957679,0.1424125,0.0441478,0.001892128],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8809475,0.0007575624,0.0910024,0.00023705,0.000167,0.0001004033,0.0004298225,0.0002212647,0.02613697],"genre_scores_gemma":[0.9965609,0.00002900746,0.001430834,0.0001121295,0.00024347,0.000004603441,0.0002928244,0.00002100497,0.001305272],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1829078,"threshold_uncertainty_score":0.7908593,"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."}}