{"id":"W4380238568","doi":"10.1016/j.ijhydene.2023.05.202","title":"Correlations for prediction of hydrogen gas viscosity and density for production, transportation, storage, and utilization applications","year":2023,"lang":"en","type":"article","venue":"International Journal of Hydrogen Energy","topic":"Carbon Dioxide Capture Technologies","field":"Engineering","cited_by":47,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; University of Calgary","funders":"Department of Chemical and Process Engineering, University of Surrey; Natural Sciences and Engineering Research Council of Canada; China Scholarship Council","keywords":"Thermodynamics; Viscosity; Hydrogen; Hydrogen storage; Absolute deviation; Triple point; Range (aeronautics); Equation of state; Materials science; Chemistry; Physics; Mathematics; Statistics; Organic chemistry","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001243032,0.0008638527,0.0004534037,0.001054886,0.0003020277,0.0005435515,0.0005991085,0.0004920498,0.001853259],"category_scores_gemma":[0.004916556,0.0003549681,0.0005226802,0.000994433,0.0003087416,0.001000644,0.0004527157,0.0009693478,0.0007087482],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004767994,"about_ca_system_score_gemma":0.00101804,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002731499,"about_ca_topic_score_gemma":0.002879918,"domain_scores_codex":[0.9993363,0.0001660853,0.00004075402,0.00008507981,0.0003254756,0.00004631761],"domain_scores_gemma":[0.9982985,0.0007994742,0.0002875937,0.0001826756,0.0004026535,0.00002912537],"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":[0.000245058,0.0003338901,0.01962808,0.000561614,0.0001211693,0.0003473112,0.0001866928,0.7144009,0.08603757,0.01424023,0.003657088,0.1602404],"study_design_scores_gemma":[0.000008173884,0.00006820304,0.002606864,0.00002772718,0.00001576387,0.00003082777,0.00002160011,0.9729998,0.02167319,0.0009091353,0.001615237,0.0000234001],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.324656,0.001937435,0.6614382,0.0003354629,0.0001863614,0.0001708919,0.001362003,0.003080414,0.006833159],"genre_scores_gemma":[0.8873942,0.001215999,0.1079366,0.00005186134,0.0000448322,0.0001886448,0.001006405,0.0002671273,0.001894296],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002731499,"threshold_uncertainty_score":0.006573796,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0162495523586161,"score_gpt":0.2380075484685235,"score_spread":0.2217579961099074,"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."}}