{"id":"W4238076008","doi":"10.2172/1361459","title":"Next Generation Hydrogen Station Composite Data Products: Retail Stations, Data through Quarter 4 of 2016","year":2017,"lang":"en","type":"report","venue":"","topic":"Transportation Systems and Infrastructure","field":"Business, Management and Accounting","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Quarter (Canadian coin); Composite number; Business; Telecommunications; Database; Commerce; Computer science; 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":[0.001581592,0.001350788,0.000650152,0.003553794,0.000601266,0.002913609,0.000974455,0.0004846934,0.04177452],"category_scores_gemma":[0.007379338,0.0006674217,0.0005393117,0.007102939,0.000235175,0.002442701,0.001065429,0.001141149,0.04968859],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003048744,"about_ca_system_score_gemma":0.01068555,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.09068595,"about_ca_topic_score_gemma":0.07354627,"domain_scores_codex":[0.9971414,0.00008057249,0.0001997607,0.0001778994,0.002224013,0.0001761789],"domain_scores_gemma":[0.9928303,0.000437225,0.0006544648,0.0003496395,0.005426495,0.0003018333],"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":[0.0001740683,0.0000750187,0.007213815,0.0002097967,0.00002489902,0.00002350377,0.00004283353,0.0007824863,0.0002875645,0.001152757,0.9731846,0.01682866],"study_design_scores_gemma":[0.00007916756,0.0001079813,0.04485993,0.0001513539,0.00003770231,0.00004872931,0.0002728257,0.0009737915,0.004825647,0.0007172278,0.947885,0.00004076316],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"other","genre_scores_codex":[0.004171577,0.0001931539,0.0004985921,0.0003532614,0.0004703997,0.0001577608,0.9613014,0.0005077566,0.03234605],"genre_scores_gemma":[0.006317818,0.0006286571,0.0009681433,0.00007557476,0.00008435365,0.0002049327,0.9397292,0.0002531714,0.05173819],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.09068595,"threshold_uncertainty_score":0.1803162,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1883175916008341,"score_gpt":0.3198767456958416,"score_spread":0.1315591540950075,"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."}}