{"id":"W2537803028","doi":"10.1016/j.molliq.2016.10.083","title":"On the prediction of interfacial tension (IFT) for water-hydrocarbon gas system","year":2016,"lang":"en","type":"article","venue":"Journal of Molecular Liquids","topic":"Enhanced Oil Recovery Techniques","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval","funders":"","keywords":"Radial basis function; Adaptive neuro fuzzy inference system; Simulated annealing; Computer science; Hydrocarbon; Support vector machine; Inference system; Soft computing; Biological system; Artificial neural network; Algorithm; Fuzzy logic; Machine learning; Artificial intelligence; Chemistry; Fuzzy control system","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":[0.0002709226,0.0003536415,0.0003253868,0.0002366321,0.0002172822,0.000370563,0.0004291741,0.0006883892,0.0005038516],"category_scores_gemma":[0.0009611195,0.00014946,0.0003583022,0.0001701705,0.0002350502,0.0005425272,0.0002658262,0.0005832182,0.0001342795],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002634889,"about_ca_system_score_gemma":0.0002614185,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005468556,"about_ca_topic_score_gemma":0.002223481,"domain_scores_codex":[0.9999192,0.00002552768,0.000003632443,0.00001937923,0.00002138441,0.00001099241],"domain_scores_gemma":[0.9997676,0.0001696816,0.00001290512,0.000009562194,0.00003405429,0.000006203072],"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":[0.0001543108,0.0001056189,0.004344796,0.0001950699,0.00003927164,0.0002602636,0.00006888175,0.8733284,0.0785666,0.005679914,0.0005103531,0.03674646],"study_design_scores_gemma":[0.000001055198,0.000009130067,0.0001384323,0.000001286785,0.000001564667,0.00000412382,0.000002188017,0.9972484,0.002411861,0.0001393226,0.00004130159,0.000001502406],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6289347,0.001664683,0.3632781,0.0004134963,0.00008106329,0.000044693,0.0001223172,0.0002942783,0.00516671],"genre_scores_gemma":[0.9896365,0.0004059839,0.008699179,0.00001799181,0.00002250997,0.00001583163,0.00006012898,0.00002205281,0.001119832],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005468556,"threshold_uncertainty_score":0.01087344,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006688635583873958,"score_gpt":0.1974871312971845,"score_spread":0.1907984957133106,"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."}}