{"id":"W4206855336","doi":"10.2172/1346538","title":"Next Generation Hydrogen Station Composite Data Products: All Stations (Retail and Non-Retail Combined), Data through Quarter 3 of 2016","year":2017,"lang":"en","type":"report","venue":"","topic":"Hermeneutics and Narrative Identity","field":"Arts and Humanities","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Quarter (Canadian coin); Composite number; Retail industry; Business; Computer science; Advertising; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004112931,0.0007709024,0.0005721137,0.00367804,0.00118763,0.003916068,0.0008509065,0.0004584737,0.09326287],"category_scores_gemma":[0.01478311,0.0006203827,0.0003863068,0.006355613,0.0005267122,0.003245926,0.002147664,0.001367432,0.06484002],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004823332,"about_ca_system_score_gemma":0.01274315,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1020777,"about_ca_topic_score_gemma":0.1289413,"domain_scores_codex":[0.9943919,0.0002221486,0.0002709336,0.0002118088,0.004638772,0.0002644952],"domain_scores_gemma":[0.9842438,0.0008838986,0.0006970023,0.0008031121,0.0127246,0.0006477486],"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":[0.0002571378,0.00009523339,0.004833939,0.0002338318,0.00001617431,0.00002002155,0.0002378426,0.0003851439,0.0003613861,0.003041939,0.9581581,0.03235919],"study_design_scores_gemma":[0.00005014987,0.00008609699,0.02423762,0.0001244033,0.00001427614,0.00002270818,0.001050816,0.0002999872,0.003016674,0.0007589154,0.970305,0.00003330172],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.01075515,0.0003414162,0.001526239,0.001093623,0.0007345609,0.0006784733,0.7756687,0.001026955,0.2081748],"genre_scores_gemma":[0.02204719,0.0008178345,0.0037862,0.0002145591,0.0001211778,0.001263052,0.6604517,0.001117533,0.3101807],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1020777,"threshold_uncertainty_score":0.3119954,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3106822481752481,"score_gpt":0.3477793173385766,"score_spread":0.0370970691633285,"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."}}