{"id":"W3091980650","doi":"10.2172/1669431","title":"Next Generation Hydrogen Station Composite Data Products: Retail Stations (Data through Quarter 3 of 2019)","year":2020,"lang":"en","type":"report","venue":"","topic":"Hybrid Renewable Energy Systems","field":"Energy","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Quarter (Canadian coin); Software deployment; Reliability (semiconductor); Composite number; Data quality; Hydrogen production; Environmental science; Hydrogen; Computer science; Engineering; Operations management; Chemistry; Operating system; Geography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001124965,0.0006352223,0.001139959,0.0002012631,0.0001637263,0.000209072,0.002353658,0.0004001998,0.0007468825],"category_scores_gemma":[0.0006425176,0.0005950101,0.00009692006,0.0007087158,0.0001067505,0.002208034,0.0009675303,0.0003803362,0.0002650929],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003045891,"about_ca_system_score_gemma":0.002604761,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.07882609,"about_ca_topic_score_gemma":0.02314967,"domain_scores_codex":[0.9929693,0.0005053224,0.00210622,0.002054322,0.001907646,0.0004571291],"domain_scores_gemma":[0.9901683,0.0001210357,0.0015431,0.006731628,0.001292177,0.0001437433],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002463631,0.0001511034,0.00009062737,0.0007950611,0.001096436,0.00002715158,0.0004112618,0.02175775,0.02303378,0.0007727276,0.9478227,0.004016791],"study_design_scores_gemma":[0.0004044337,0.000076966,0.0000238885,0.000165035,0.0004710406,0.00005986036,0.0002015194,0.06412832,0.008860508,0.00006170676,0.9248632,0.0006835321],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"dataset","genre_scores_codex":[0.008376291,0.02320654,0.08650263,0.01129489,0.0183643,0.006751935,0.06860022,0.002395314,0.7745079],"genre_scores_gemma":[0.08246051,0.005450477,0.02797591,0.000240185,0.007324356,0.0001069192,0.8178577,0.0004373808,0.05814658],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.7492574,"threshold_uncertainty_score":0.9996501,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2142749729234893,"score_gpt":0.3289650136189408,"score_spread":0.1146900406954515,"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."}}