{"id":"W4413383277","doi":"10.1002/cjce.70064","title":"Application of machine learning in modelling gas dispersion coefficients for hydrogen storage in porous media","year":2025,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Hybrid Renewable Energy Systems","field":"Energy","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"","keywords":"Porous medium; Hydrogen storage; Dispersion (optics); Porosity; Hydrogen; Materials science; Computer science; Chemical engineering; Process engineering; Environmental science; Chemistry; Physics; Engineering; Composite material; Optics; Organic chemistry","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.001023677,0.0006401107,0.0003711131,0.0004758291,0.0001570668,0.0005536666,0.0004232182,0.0007473354,0.0003946413],"category_scores_gemma":[0.002326084,0.000230136,0.0004048412,0.0003388533,0.000290503,0.0005036802,0.0002899277,0.0005977554,0.0001203158],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004179643,"about_ca_system_score_gemma":0.0005067438,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005587471,"about_ca_topic_score_gemma":0.003068114,"domain_scores_codex":[0.9998206,0.00007815051,0.00001434507,0.00003584408,0.0000361948,0.00001485456],"domain_scores_gemma":[0.9988822,0.0008252693,0.0001073409,0.00004189948,0.0001277465,0.00001556599],"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":[0.00002160865,0.00003760054,0.001686778,0.0000198785,0.00001302808,0.00001523412,0.00001052372,0.983327,0.00291688,0.0002088667,0.00005932456,0.01168334],"study_design_scores_gemma":[5.450182e-7,0.000005578847,0.00007659573,7.703769e-7,7.04686e-7,8.323074e-7,0.000001117051,0.9992913,0.0005668323,0.00003868866,0.00001578634,0.000001104318],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5969978,0.0005450242,0.399671,0.0002959867,0.00003367531,0.00005940138,0.0001471779,0.0008223422,0.001427537],"genre_scores_gemma":[0.9833463,0.00007887239,0.01623533,0.00001539827,0.000005175585,0.00002575728,0.00004989928,0.000009442954,0.0002338814],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005587471,"threshold_uncertainty_score":0.01110989,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006019893831700283,"score_gpt":0.1861777089239948,"score_spread":0.1801578150922945,"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."}}