{"id":"W3093447293","doi":"10.1016/j.ijhydene.2020.09.108","title":"Development of risk mitigation guidance for sensor placement inside mechanically ventilated enclosures – Phase 1","year":2020,"lang":"en","type":"article","venue":"International Journal of Hydrogen Energy","topic":"Gas Sensing Nanomaterials and Sensors","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"Office of Energy Efficiency; Joint Research Centre; U.S. Department of Energy; Office of Energy Efficiency and Renewable Energy; National Renewable Energy Laboratory; European Commission","keywords":"Software deployment; Environmental science; Computer science; Hazard; Reduction (mathematics); Warning system; Hazard analysis; Enclosure; Reliability engineering; Risk analysis (engineering); Engineering; Telecommunications","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":[0.0008323118,0.00103944,0.000502449,0.0004168677,0.0003731434,0.0008076894,0.001168367,0.001184568,0.001908951],"category_scores_gemma":[0.002523562,0.0004298825,0.0004109177,0.0001547097,0.0003301128,0.0009350606,0.001205076,0.0007546293,0.0009408143],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004168383,"about_ca_system_score_gemma":0.001863847,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001422028,"about_ca_topic_score_gemma":0.002117404,"domain_scores_codex":[0.9992722,0.0001271277,0.00002777306,0.0001266196,0.0003846466,0.00006170142],"domain_scores_gemma":[0.9987324,0.0002728821,0.0002170235,0.0001347681,0.0005923439,0.00005065718],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003827018,0.0002373755,0.004377102,0.0004893501,0.00004315903,0.0003623542,0.0003321154,0.4143264,0.3197291,0.008412683,0.003070933,0.2482367],"study_design_scores_gemma":[0.00003313358,0.0007377352,0.002500043,0.00009424144,0.00003001485,0.0002101108,0.0001665959,0.830193,0.1532341,0.003317595,0.009435903,0.00004743559],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04271829,0.0001973971,0.9513509,0.0001996999,0.00005702921,0.00015852,0.00009972409,0.001088295,0.004130155],"genre_scores_gemma":[0.4620807,0.0002552632,0.5339659,0.00008302261,0.00002066232,0.0001677522,0.0002519633,0.0001298793,0.003044965],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001908951,"threshold_uncertainty_score":0.006386101,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01505381853094138,"score_gpt":0.2458089120990678,"score_spread":0.2307550935681264,"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."}}