{"id":"W4388039187","doi":"10.1016/j.ijhydene.2023.10.110","title":"Contribution to modeling hydrogen permeation and thickness optimization in blow molded plastic liners for on-board compressed hydrogen tanks","year":2023,"lang":"en","type":"article","venue":"International Journal of Hydrogen Energy","topic":"Membrane Separation and Gas Transport","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Montréal; National Research Council Canada","funders":"","keywords":"Permeation; Hydrogen; Materials science; Permeability (electromagnetism); Polymer; Composite material; Diffusion; Blow molding; Mechanical engineering; Chemical engineering; Thermodynamics; Chemistry; Engineering; Membrane; Physics; 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":[0.000286509,0.0001759783,0.000255817,0.0006420115,0.00004228971,0.0000446682,0.0002141925,0.0001085387,0.00002612316],"category_scores_gemma":[0.0001075214,0.0001812108,0.00009925066,0.000247085,0.00001440199,0.000220445,0.00001455913,0.0001205756,0.000004789106],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001367763,"about_ca_system_score_gemma":0.00003962358,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001803886,"about_ca_topic_score_gemma":0.00003954165,"domain_scores_codex":[0.9985378,0.00003132446,0.000611229,0.0001714531,0.0004398718,0.0002083129],"domain_scores_gemma":[0.999192,0.0001316141,0.0001090486,0.00008370289,0.0003554968,0.0001281252],"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":[0.0002273134,0.00003069127,0.0001111597,0.00001126851,0.0001339119,0.00002970811,0.0001731424,0.9714863,0.0266916,0.0005211231,0.00006787549,0.0005158762],"study_design_scores_gemma":[0.001592096,0.000081263,0.00004934572,0.000100431,0.0000280274,0.00003279846,0.00002736232,0.9700284,0.02672929,0.0002708992,0.0008926616,0.0001674469],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7903249,0.00007589251,0.2084235,0.0003363125,0.0005585242,0.0001289923,0.00003704143,0.00006631337,0.00004849163],"genre_scores_gemma":[0.9983599,0.0001990359,0.0007244641,0.0001693827,0.0002425276,0.00003145237,0.0002068371,0.00003625976,0.0000301705],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.208035,"threshold_uncertainty_score":0.7389562,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01366188466861327,"score_gpt":0.2503818375901664,"score_spread":0.2367199529215531,"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."}}