{"id":"W1995360044","doi":"10.1002/cjce.5450840301","title":"Molecular Modelling—an Enabling Technology for Chemical Engineers","year":2006,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Advanced Physical and Chemical Molecular Interactions","field":"Chemistry","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Polyolefin; Statistical physics; Miscibility; Molecular dynamics; Scale (ratio); Monte Carlo method; Set (abstract data type); Field (mathematics); Force field (fiction); Computer science; Mixing (physics); Nanotechnology; Materials science; Physics; Mathematics; Chemistry; Computational chemistry; Polymer; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.00004429169,0.0001889159,0.0002339887,0.0001044601,0.00005684631,0.0000423644,0.0004442892,0.0001798369,0.00003229083],"category_scores_gemma":[0.0001342013,0.0001593063,0.0002036962,0.0001905131,0.00008675985,0.00009379051,0.00001511215,0.0006152923,0.000001579549],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002014634,"about_ca_system_score_gemma":0.0001288854,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001633527,"about_ca_topic_score_gemma":0.000007621057,"domain_scores_codex":[0.9989133,0.000002259419,0.0003575909,0.0001543938,0.0001463399,0.0004260855],"domain_scores_gemma":[0.999092,0.00009043299,0.00009972751,0.0002172719,0.0001741539,0.0003263698],"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":[0.000008812746,0.0000146048,0.000001365998,0.00002242237,0.00002905199,0.000016295,0.00001815354,0.2062667,0.7877721,0.005567809,0.00004851158,0.0002341149],"study_design_scores_gemma":[0.0002510994,0.000009399264,4.689486e-8,0.00005677847,0.00004304086,0.00009848618,0.00001409728,0.07206122,0.9152073,0.007029465,0.005049435,0.0001796883],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9467818,0.0004223907,0.0516615,0.0006587282,0.00009639597,0.00005048985,0.00001669735,0.00004897986,0.0002630368],"genre_scores_gemma":[0.993409,5.921452e-7,0.006000073,0.00004451125,0.0004498697,0.00001291373,0.0000158729,0.00004653568,0.00002060314],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1342055,"threshold_uncertainty_score":0.6496325,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005294396111409349,"score_gpt":0.1931640514522946,"score_spread":0.1878696553408853,"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."}}