{"id":"W2807335013","doi":"10.1002/cjce.23254","title":"Modelling asphaltene precipitation titration data: A committee of machines and a group method of data handling","year":2018,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Petroleum Processing and Analysis","field":"Chemistry","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Asphaltene; Particle swarm optimization; Artificial neural network; Computer science; Multilayer perceptron; Algorithm; Artificial intelligence; Chemistry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003040374,0.0009212954,0.001268796,0.0007220013,0.0005315385,0.0009623056,0.001353339,0.001408385,0.0008413342],"category_scores_gemma":[0.006006464,0.0004659589,0.001193651,0.0009475568,0.0006055122,0.001412284,0.0006024571,0.001159678,0.0003393139],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008293616,"about_ca_system_score_gemma":0.001202951,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01082125,"about_ca_topic_score_gemma":0.004332405,"domain_scores_codex":[0.9981401,0.0007141522,0.0001366024,0.0004526597,0.0004363368,0.0001201247],"domain_scores_gemma":[0.9978101,0.0009720023,0.0002043931,0.0002525883,0.0007083377,0.00005271248],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001425435,0.0001049403,0.003353154,0.00007716929,0.0001234739,0.00005942391,0.0001269843,0.8290341,0.004522154,0.002978917,0.0007947417,0.1586824],"study_design_scores_gemma":[0.000003121886,0.0000245429,0.0002751269,0.000001953938,0.000006335209,0.000006390148,0.000005293654,0.9983243,0.0008190329,0.0003180502,0.0002109489,0.000004924882],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05593206,0.000210836,0.9421326,0.0001033227,0.00004787699,0.0000944103,0.00003758761,0.0004799558,0.0009614371],"genre_scores_gemma":[0.7152758,0.000209437,0.281201,0.00009413524,0.000064617,0.0003585847,0.0001577932,0.00005903696,0.002579601],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01082125,"threshold_uncertainty_score":0.02151656,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03725567542648751,"score_gpt":0.2718097153263182,"score_spread":0.2345540398998307,"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."}}