{"id":"W3023489600","doi":"10.3390/nano10050890","title":"Artificial Intelligence Based Methods for Asphaltenes Adsorption by Nanocomposites: Application of Group Method of Data Handling, Least Squares Support Vector Machine, and Artificial Neural Networks","year":2020,"lang":"en","type":"article","venue":"Nanomaterials","topic":"Petroleum Processing and Analysis","field":"Chemistry","cited_by":65,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland; University of Calgary","funders":"","keywords":"Asphaltene; Artificial neural network; Support vector machine; Least squares support vector machine; Group (periodic table); Artificial intelligence; Adsorption; Nanocomposite; Machine learning; Group contribution method; Computer science; Pattern recognition (psychology); Materials science; Data mining; Engineering; Chemical engineering; Chemistry; Nanotechnology; Organic chemistry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001026209,0.0008129122,0.0007373309,0.0009031421,0.0002314345,0.0006281165,0.0007261283,0.0007724267,0.0004269691],"category_scores_gemma":[0.002158046,0.0002994016,0.000895097,0.0009046958,0.0003853953,0.0009034648,0.0003917505,0.001016137,0.0001411157],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000386828,"about_ca_system_score_gemma":0.0005807979,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002225446,"about_ca_topic_score_gemma":0.001650545,"domain_scores_codex":[0.9994306,0.0001675187,0.00005226206,0.0001020539,0.0002287047,0.00001878014],"domain_scores_gemma":[0.9992892,0.0003457146,0.0001130965,0.00005478347,0.0001838051,0.00001350251],"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":[0.0001220046,0.0001583727,0.003449758,0.0008322012,0.0002403932,0.0001165591,0.0001782024,0.5140385,0.02537536,0.01344244,0.001363919,0.4406822],"study_design_scores_gemma":[0.000004601174,0.00003470561,0.0004366443,0.0000095063,0.0000106743,0.00002107095,0.00001230883,0.9923816,0.004474546,0.001736076,0.0008651363,0.00001303961],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0185701,0.001261048,0.9786027,0.0001683225,0.0000486926,0.00004662464,0.00004100016,0.000372132,0.0008894889],"genre_scores_gemma":[0.457499,0.002446146,0.5368194,0.0001025115,0.00008717833,0.0003722897,0.0002027347,0.00008813861,0.00238265],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002225446,"threshold_uncertainty_score":0.005427182,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05541223196336875,"score_gpt":0.3630065696224157,"score_spread":0.307594337659047,"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."}}