{"id":"W4255487892","doi":"10.2172/1412798","title":"Next Generation Hydrogen Station Composite Data Products: Retail Stations, Data through Quarter 2 of 2017","year":2017,"lang":"en","type":"report","venue":"","topic":"Hybrid Renewable Energy Systems","field":"Energy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Quarter (Canadian coin); Composite number; Hydrogen; Telecommunications; Engineering; Materials science; Geography; Chemistry; Composite material; Archaeology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001148847,0.001201233,0.0005344679,0.002901197,0.0006154009,0.003177381,0.0009986759,0.0005966403,0.06965644],"category_scores_gemma":[0.005158772,0.0006090419,0.0005043212,0.005826251,0.0002306646,0.00234622,0.0009854828,0.001250425,0.07448474],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002319298,"about_ca_system_score_gemma":0.007259182,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05623889,"about_ca_topic_score_gemma":0.05254301,"domain_scores_codex":[0.9981365,0.00005179182,0.0001035309,0.0001173703,0.001465635,0.0001250986],"domain_scores_gemma":[0.9955547,0.0003368859,0.0003267702,0.0002226554,0.003360755,0.00019825],"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":[0.0001209239,0.00005593264,0.002590058,0.0001727669,0.00001505899,0.00001823164,0.00002554685,0.000416159,0.0002578381,0.0008575785,0.9806839,0.01478593],"study_design_scores_gemma":[0.00005455968,0.00006686839,0.01386707,0.0001148572,0.00002350783,0.00003132531,0.0001892187,0.0005297813,0.003089455,0.0005662383,0.9814407,0.00002641133],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.003584471,0.0003753417,0.0008775081,0.0006233485,0.000713751,0.0002034734,0.9235138,0.000777742,0.06933059],"genre_scores_gemma":[0.006213439,0.001032663,0.001197039,0.0001663206,0.0001240054,0.0002694377,0.9059632,0.0005859602,0.08444794],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.06965644,"threshold_uncertainty_score":0.233024,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2820134827987041,"score_gpt":0.3607025730219813,"score_spread":0.07868909022327725,"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."}}