{"id":"W3014981226","doi":"10.1016/j.ijhydene.2020.03.040","title":"Stochastic explosion risk analysis of hydrogen production facilities","year":2020,"lang":"en","type":"article","venue":"International Journal of Hydrogen Energy","topic":"Risk and Safety Analysis","field":"Decision Sciences","cited_by":50,"is_retracted":false,"has_abstract":false,"ca_institutions":"Memorial University of Newfoundland","funders":"Qingdao Municipal Science and Technology Bureau; National Key Research and Development Program of China Stem Cell and Translational Research; China Postdoctoral Science Foundation; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Computational fluid dynamics; Overpressure; Computer science; Hydrogen production; Sensitivity (control systems); Parametric statistics; Bayesian probability; Uncertainty quantification; Dispersion (optics); Environmental science; Hydrogen; Statistics; Machine learning; Engineering; Mathematics; Artificial intelligence; Mechanics; Physics","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":[0.00358612,0.001052184,0.001746108,0.001445228,0.0003981155,0.001837928,0.001477427,0.001468645,0.002371565],"category_scores_gemma":[0.008346483,0.0008939889,0.001394673,0.0008702012,0.001402673,0.001492069,0.001499166,0.001480123,0.0001270312],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002217332,"about_ca_system_score_gemma":0.001492346,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00648299,"about_ca_topic_score_gemma":0.002609332,"domain_scores_codex":[0.9984999,0.0007557833,0.00004726719,0.0001430594,0.0003258048,0.0002282804],"domain_scores_gemma":[0.9926754,0.00555455,0.0007581052,0.0001638266,0.0005638629,0.000284351],"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":[0.00006708222,0.00002926073,0.0005443408,0.00002675037,0.00004502223,0.00004938523,0.00001784355,0.9787952,0.0002930243,0.01797297,0.0002578017,0.001901219],"study_design_scores_gemma":[0.000006742305,0.00002467082,0.0002892907,0.000004379413,0.00001339557,0.00001208876,0.00001026692,0.9917588,0.00007615369,0.007718993,0.00007874307,0.000006490086],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2867907,0.001743863,0.6943667,0.001851315,0.00009963059,0.0001395313,0.0004742908,0.0002082183,0.01432585],"genre_scores_gemma":[0.9878677,0.0003874862,0.006750518,0.00004855844,0.00005831059,0.00005519925,0.0001683014,0.00002326631,0.004640674],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00648299,"threshold_uncertainty_score":0.01896542,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05752321754158584,"score_gpt":0.3214903447587324,"score_spread":0.2639671272171466,"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."}}