{"id":"W3160528426","doi":"10.2172/1782589","title":"Next Generation Hydrogen Station Composite Data Products: Retail Stations; Summer 2020 (Q2 FY2020)","year":2021,"lang":"en","type":"report","venue":"","topic":"Data Quality and Management","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Renewable Energy Laboratory","keywords":"Software deployment; Reliability (semiconductor); Composite number; Quarter (Canadian coin); Hydrogen production; Environmental science; Data quality; Component (thermodynamics); Hydrogen; Environmental economics; Computer science; Business; Service (business); Marketing; Chemistry; 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":["metaresearch","metaepi_narrow","scholarly_communication","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0131063,0.0004392371,0.0007852314,0.0004037006,0.0003674903,0.002577473,0.002632683,0.0002452641,0.00566053],"category_scores_gemma":[0.009668946,0.0003686595,0.0001283511,0.00173188,0.0001093807,0.002934718,0.002171003,0.0003925968,0.001739059],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002512921,"about_ca_system_score_gemma":0.002179047,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001720911,"about_ca_topic_score_gemma":0.005472715,"domain_scores_codex":[0.9872941,0.001017564,0.002324223,0.002618497,0.006332138,0.0004134729],"domain_scores_gemma":[0.9890642,0.0006202065,0.00128527,0.005663077,0.003183858,0.0001833573],"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":[0.000008278816,0.0001123147,0.0001179224,0.00008592108,0.0001684172,0.0000497137,0.0001427286,0.0001579227,0.0005884937,0.0004746134,0.9132497,0.08484403],"study_design_scores_gemma":[0.0001883758,0.00002910242,0.0002773337,0.00003853241,0.0002046852,0.00001673351,0.0008789503,0.00621034,0.0004399306,0.0005948185,0.990658,0.0004632128],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.01352018,0.02514844,0.2631302,0.07626233,0.02644527,0.01016828,0.05567382,0.001090415,0.5285611],"genre_scores_gemma":[0.008203316,0.01462397,0.0423229,0.003512734,0.004540578,0.0001622449,0.4545029,0.0001441359,0.4719872],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.3988291,"threshold_uncertainty_score":0.9998766,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.670532853712078,"score_gpt":0.4818620424417646,"score_spread":0.1886708112703135,"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."}}