{"id":"W2088551265","doi":"10.1016/j.fluid.2004.06.026","title":"Large scale data regression for process calculations—theory and description of the virtual database","year":2004,"lang":"en","type":"article","venue":"Fluid Phase Equilibria","topic":"Phase Equilibria and Thermodynamics","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"National Institute of Standards and Technology; Virtual Materials Group","keywords":"Database; Consistency (knowledge bases); Process (computing); Data mining; Documentation; Component (thermodynamics); Quality (philosophy); Computer science; Chemistry; Thermodynamics; Artificial intelligence","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":[0.0004802345,0.0001813266,0.0001979035,0.00006242588,0.0001084715,0.00003140401,0.0004188878,0.00008471992,0.00002663776],"category_scores_gemma":[0.0001222709,0.0001399643,0.00005027444,0.000200827,0.00008891372,0.0007574804,0.0001963877,0.0001054785,0.000002643249],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003705401,"about_ca_system_score_gemma":0.0000638472,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003039068,"about_ca_topic_score_gemma":0.00001665595,"domain_scores_codex":[0.9989332,0.00005034588,0.000299035,0.0002831189,0.0001749979,0.000259321],"domain_scores_gemma":[0.9988704,0.00008505773,0.00005885481,0.0008528313,0.00004625118,0.00008662129],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003199282,0.0002333806,0.00008223211,0.0002186713,0.00005758693,0.00000171811,0.001050181,0.001254221,0.9801776,0.01515955,0.0004125989,0.00103231],"study_design_scores_gemma":[0.006745894,0.0001246807,0.000172195,0.0003747161,0.0001436398,0.00002187567,0.0002614722,0.914098,0.06028973,0.01601981,0.001399481,0.0003485752],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9249209,0.0003999164,0.07139098,0.00003128424,0.0004359285,0.0004665088,0.002089896,0.0001179322,0.0001466116],"genre_scores_gemma":[0.9982175,0.0000193159,0.0005775504,0.00002363071,0.0001363692,0.00002490322,0.0008854163,0.00004798953,0.00006734979],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9198879,"threshold_uncertainty_score":0.570758,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02719661546210317,"score_gpt":0.2998715098407914,"score_spread":0.2726748943786882,"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."}}