{"id":"W2149534170","doi":"10.1002/masy.200450239","title":"Process modelling and optimization of styrene polymerization","year":2004,"lang":"en","type":"article","venue":"Macromolecular Symposia","topic":"Thermal and Kinetic Analysis","field":"Materials Science","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Polystyrene; Bifunctional; Styrene; Polymerization; Materials science; Process (computing); Process engineering; Polymer chemistry; Computer science; Copolymer; Polymer; Chemistry; Catalysis; Organic chemistry; 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":[0.00006314144,0.0000759876,0.0001171943,0.00004467686,0.00004866848,0.00002341707,0.00007680397,0.00003496139,0.00008825296],"category_scores_gemma":[0.000004308848,0.00006960457,0.00002507897,0.0001496458,0.00005040927,0.00007485609,0.00002182904,0.00002009677,0.00000363934],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008183109,"about_ca_system_score_gemma":0.00002004351,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000760976,"about_ca_topic_score_gemma":9.600132e-7,"domain_scores_codex":[0.9994156,0.00001962973,0.0001591156,0.0001614764,0.0001397904,0.0001044335],"domain_scores_gemma":[0.999725,0.000003113714,0.0000796426,0.000104996,0.00004932211,0.00003791212],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000006168587,0.00001476405,0.00002156154,0.00001318696,0.000003918968,0.000001797629,0.0001602635,0.5455754,0.453228,0.0009257906,4.664668e-8,0.00004904336],"study_design_scores_gemma":[0.0002762023,0.00004253097,0.00002686108,0.0000308498,0.00004501723,0.000009057159,0.00003652638,0.08774413,0.9112206,0.0004698225,0.000003312877,0.0000951146],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5219635,0.0002114637,0.4775839,0.00004078682,0.00001341892,0.00003915544,0.00000285005,0.00001517489,0.0001297375],"genre_scores_gemma":[0.9890773,0.00003170831,0.01080652,0.0000330443,0.00001222206,0.000004226835,0.00001161692,0.00001055995,0.00001275918],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4671139,"threshold_uncertainty_score":0.2838392,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005267369231719721,"score_gpt":0.2082080413977969,"score_spread":0.2029406721660771,"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."}}