{"id":"W4387310771","doi":"10.1016/j.ijhydene.2023.08.288","title":"Predicting hydrogen storage requirements through the natural gas market for a low-emission future","year":2023,"lang":"en","type":"article","venue":"International Journal of Hydrogen Energy","topic":"Hybrid Renewable Energy Systems","field":"Energy","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Innovates","keywords":"Environmental science; Hydrogen storage; Compressed natural gas; Natural gas storage; Natural gas; Energy storage; Footprint; Waste management; Zero emission; Liquefied natural gas; Compressed air energy storage; Reduction (mathematics); Environmental engineering; Process engineering; Hydrogen; Engineering; Chemistry; Geology; Physics; Mathematics","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":[0.0004531539,0.0003307176,0.0002544097,0.0004071213,0.0002808724,0.0009532551,0.0004830877,0.0009891184,0.003925893],"category_scores_gemma":[0.001548189,0.0002916166,0.0003973911,0.0004427299,0.0002652811,0.002793314,0.0002437059,0.0008248452,0.000436635],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00134653,"about_ca_system_score_gemma":0.0008250849,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02164122,"about_ca_topic_score_gemma":0.02980781,"domain_scores_codex":[0.9999368,0.00001042826,0.000003324138,0.00001856685,0.00001604793,0.00001471857],"domain_scores_gemma":[0.9993037,0.0004165961,0.00008891584,0.00002320628,0.0001124706,0.00005512082],"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":[0.0007533407,0.0001715212,0.07032913,0.0001054097,0.00006320229,0.0005166613,0.00008193216,0.8901919,0.004852948,0.01392913,0.005245529,0.01375928],"study_design_scores_gemma":[0.00001276878,0.00005104922,0.01217018,0.00000566848,0.00001106296,0.00003260315,0.0001011778,0.9814851,0.001119174,0.003712076,0.001283867,0.00001518307],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9871656,0.0001620092,0.003312762,0.0007565016,0.00002056777,0.00001658371,0.002537077,0.00005753534,0.00597136],"genre_scores_gemma":[0.9972603,0.00007335643,0.000790779,0.0000211817,0.000008797831,0.000005916789,0.0006783406,0.0000163582,0.001144858],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02164122,"threshold_uncertainty_score":0.0430305,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01433627292119659,"score_gpt":0.2709199089209473,"score_spread":0.2565836359997507,"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."}}