{"id":"W2535209849","doi":"10.1016/j.molliq.2016.10.112","title":"Predictive model based on ANFIS for estimation of thermal conductivity of carbon dioxide","year":2016,"lang":"en","type":"article","venue":"Journal of Molecular Liquids","topic":"Phase Equilibria and Thermodynamics","field":"Engineering","cited_by":33,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval","funders":"","keywords":"Adaptive neuro fuzzy inference system; Thermal conductivity; Particle swarm optimization; Experimental data; Supercritical fluid; Thermal; Supercritical carbon dioxide; Computer science; Neuro-fuzzy; Fuzzy logic; Materials science; Machine learning; Mathematics; Artificial intelligence; Thermodynamics; Fuzzy control system; Statistics; Physics","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":[0.0003543691,0.0004749419,0.0005029363,0.0002641414,0.0002821893,0.0004731969,0.0005127044,0.0005981463,0.000706092],"category_scores_gemma":[0.0007401969,0.0002487397,0.000373698,0.0001997196,0.0002450464,0.0004229899,0.0002264426,0.0006503236,0.00009970805],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003568726,"about_ca_system_score_gemma":0.0003862967,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01036639,"about_ca_topic_score_gemma":0.006942934,"domain_scores_codex":[0.9998599,0.0000332585,0.00001022056,0.0000347131,0.0000459234,0.00001596489],"domain_scores_gemma":[0.999745,0.0001540052,0.00002525307,0.0000119229,0.00005818671,0.000005684215],"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":[0.0001118952,0.00006652241,0.0008875463,0.0001002982,0.00003774054,0.00008359063,0.00005063369,0.9589932,0.009338901,0.0007553179,0.0003123168,0.02926205],"study_design_scores_gemma":[0.000001665897,0.00001008351,0.0001307183,0.000001675039,0.000002962827,0.000003327964,0.000002101705,0.998998,0.000736698,0.00007443609,0.00003685749,0.000001422496],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3485145,0.0009760134,0.6418874,0.0003196772,0.000158952,0.00008322854,0.0001647818,0.00102342,0.006872026],"genre_scores_gemma":[0.9930126,0.0001080305,0.006091821,0.00001529617,0.00000959199,0.00003360713,0.00004270714,0.000007375315,0.0006789801],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01036639,"threshold_uncertainty_score":0.02061206,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00807829951546406,"score_gpt":0.2254745972221792,"score_spread":0.2173962977067151,"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."}}