{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009523696,0.0002073811,0.0005928548,0.00005126485,0.00008215933,0.0000768092,0.0003905306,0.0001537233,0.0001069882],"category_scores_gemma":[0.0002020975,0.0001951411,0.0000912544,0.0001757653,0.00008584355,0.0001692509,0.00009705421,0.00005919489,5.944064e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001103142,"about_ca_system_score_gemma":0.00003219918,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008860729,"about_ca_topic_score_gemma":0.0000076315,"domain_scores_codex":[0.9980724,0.0001617677,0.0008789102,0.0005335548,0.000146983,0.0002063125],"domain_scores_gemma":[0.99848,0.0003372957,0.0006320425,0.0003560364,0.000104702,0.0000899076],"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":[0.0003770517,0.00007279015,0.0001114555,0.0004534984,0.0000418998,1.614518e-7,0.00004679693,0.000448161,0.7766129,0.00006333025,0.00001517531,0.2217568],"study_design_scores_gemma":[0.00006991636,0.00005698126,0.000004158293,0.00001929773,0.0001339471,0.000001012526,0.0000243348,0.4441425,0.5551698,0.00006827121,0.0002093969,0.0001004509],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09757654,0.0004148917,0.9008782,0.0001425859,0.0000630538,0.00006813222,0.0007896028,0.00005506468,0.00001193907],"genre_scores_gemma":[0.8961832,0.00001398966,0.1016702,0.00004791874,0.0002546319,0.00005029494,0.001746237,0.00002640398,0.000007104243],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.799208,"threshold_uncertainty_score":0.7957622,"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."}}